Wide-View LIDAR with Areas of Special Attention

ABSTRACT

A system and method include scanning a light detection and ranging (LIDAR) device through a range of orientations corresponding to a scanning zone while emitting light pulses from the LIDAR device. The method also includes receiving returning light pulses corresponding to the light pulses emitted from the LIDAR device and determining initial point cloud data based on time delays between emitting the light pulses and receiving the corresponding returning light pulses and the orientations of the LIDAR device. The initial point cloud data has an initial angular resolution. The method includes identifying, based on the initial point cloud data, a reflective feature in the scanning zone and determining an enhancement region and an enhanced angular resolution for a subsequent scan to provide a higher spatial resolution in at least a portion of subsequent point cloud data from the subsequent scan corresponding to the reflective feature.

CROSS-REFERENCE TO RELATED APPLICATION

The present non-provisional patent application claims priority to U.S.patent application Ser. No. 13/627,623 filed on Sep. 26, 2012, and U.S.patent application Ser. No. 15/170,470 filed on Jun. 1, 2016, each ofthe contents of which are hereby incorporated by reference.

BACKGROUND

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

Vehicles can be configured to operate in an autonomous mode in which thevehicle navigates through an environment with little or no input from adriver. Such autonomous vehicles can include one or more sensors thatare configured to detect information about the environment in which thevehicle operates. The vehicle and its associated computer-implementedcontroller use the detected information to navigate through theenvironment. For example, if the sensor(s) detect that the vehicle isapproaching an obstacle, as determined by the computer-implementedcontroller, the controller adjusts the vehicle's directional controls tocause the vehicle to navigate around the obstacle.

One such sensor is a light detection and ranging (LIDAR) device. A LIDARactively estimates distances to environmental features while scanningthrough a scene to assembly a cloud of point positions indicative of thethree-dimensional shape of the environmental scene. Individual pointsare measured by generating a laser pulse and detecting a returningpulse, if any, reflected from an environmental object, and determiningthe distance to the reflective object according to the time delaybetween the emitted pulse and the reception of the reflected pulse. Thelaser, or set of lasers, can be rapidly and repeatedly scanned across ascene to provide continuous real-time information on distances toreflective objects in the scene. Combining the measured distances andthe orientation of the laser(s) while measuring each distance allows forassociating a three-dimensional position with each returning pulse. Athree-dimensional map of points of reflective features is generatedbased on the returning pulses for the entire scanning zone. Thethree-dimensional point map thereby indicates positions of reflectiveobjects in the scanned scene.

The angular resolution of a LIDAR system is defined by at least twoparameters, the effective solid angle of each emitted light pulse, andthe angular separation between each adjacent measurement point. Thesolid angle defined by each emitted light pulse is influenced by thenarrowness of the emitted pulse (e.g., the amount of beam divergence)and also by atmospheric scattering effects, potential diffraction on theenvironmental reflective surfaces, etc. The angular separation betweenadjacent measurement points is influenced by the timing budget of theLIDAR system (e.g., the allowable refresh rate for complete scans of thescene), the total solid angle of the scene being scanned. In somesystems lenses are employed to partially diverge emitted pulses suchthat the solid angle of each emitted pulse is comparable to the angularseparation between adjacent points. Diverging the emitted pulses createsbroader, less precise, individual measurement points, but allows eachmeasurement point to sample a broader angular region of the scene andthereby avoid missing features situated between adjacent measurementpoints.

SUMMARY

A LIDAR device configured to provide dynamically adjustable angularresolution is disclosed herein. The LIDAR device is driven to provideenhanced angular resolution of identified regions of a scanning zone byadjusting one or both of its laser pulse rate or beam slew rate. Regionsfor enhanced resolution scanning are identified according to techniquesto select regions of the environmental scene where enhanced resolutionscans will inform navigational determinations, object detection,obstacle avoidance, etc. Techniques are disclosed to identify: edges ofperceived objects; moving objects and/or predicted locations thereof;distant objects; objects that lack sufficient resolution to allowreliable identification; and/or objects not present in a prior baselinescan of the same scene. Modifying one or both of the angular rate ofchange or the pulse rate modifies the amount of angular change betweeneach successive pulse emitted from the LIDAR sensor, and therebymodifies the angular resolution of the point cloud output from the LIDARsystem. In some examples, a second LIDAR device provides high resolutionscanning of regions identified according to point cloud information froma first LIDAR device providing wide-view scanning resolutions.

In an aspect, a method is disclosed. The method includes scanning alight detection and ranging (LIDAR) device through a range oforientations corresponding to a scanning zone while emitting lightpulses from the LIDAR device. The method also includes receivingreturning light pulses corresponding to the light pulses emitted fromthe LIDAR device. The method further includes determining initial pointcloud data based on time delays between emitting the light pulses andreceiving the corresponding returning light pulses and the orientationsof the LIDAR device. The initial point cloud data has an initial angularresolution. The method also includes identifying, based on the initialpoint cloud data, a reflective feature in the scanning zone. The methodyet further includes locating at least one edge of the reflectivefeature. The method also includes, based on the located at least oneedge of the reflective feature, determining an enhancement region and anenhanced angular resolution for a subsequent scan, wherein the enhancedangular resolution is determined so as to provide a higher spatialresolution in at least a portion of subsequent point cloud data from thesubsequent scan, wherein the portion corresponds to the reflectivefeature.

In an aspect, an autonomous vehicle system is provided. The systemincludes a light detection and ranging (LIDAR) device including a lightsource configured to be scanned through a range of orientations directedto a scanning zone while emitting light pulses and a light detectorconfigured to receive returning light pulses reflected from features inthe scanning zone, if any, each of the returning light pulsescorresponding to an emitted light pulse. The system also includes acontroller configured to cause the LIDAR device to scan the scanningzone while emitting light pulses at a first pulse rate. The controlleris also configured to receive information from the LIDAR deviceindicative of the time delays between the emission of the light pulsesand the reception of the corresponding returning light pulses. Thecontroller is further configured to determine, based on the time delaysand orientations of the LIDAR device associated with each time delay,initial point cloud data having an initial angular resolution. Thecontroller is also configured to identify, based on the initial pointcloud data, a reflective feature in the scanning zone and locate atleast one edge of the reflective feature. The controller is furtherconfigured to, based on the located at least one edge of the reflectivefeature, determine an enhancement region and an enhanced angularresolution for a subsequent scan. The enhanced angular resolution isdetermined so as to provide a higher spatial resolution in at least aportion of subsequent point cloud data from the subsequent scan. Theportion corresponds to the reflective feature.

These as well as other aspects, advantages, and alternatives, willbecome apparent to those of ordinary skill in the art by reading thefollowing detailed description, with reference where appropriate to theaccompanying drawings.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a functional block diagram depicting aspects of an autonomousvehicle.

FIG. 2 depicts exterior views of the autonomous vehicle.

FIG. 3A provides an example depiction of a LIDAR device including beamsteering optics.

FIG. 3B symbolically illustrates a LIDAR device scanning across anobstacle-filled environmental scene.

FIG. 3C symbolically illustrates a point cloud corresponding to theobstacle-filled environmental scene of FIG. 3B.

FIGS. 4A is a schematic drawing of an example roadway approaching anintersection.

FIG. 4B is a rendering of a LIDAR-indicated point cloud corresponding tothe scene pictured in FIG. 4A.

FIGS. 4C is a schematic drawing of another example roadway.

FIG. 4D is a rendering of a LIDAR-indicated point cloud corresponding tothe scene pictured in FIG. 4C.

FIG. 5A is a flowchart of a process for adaptively adjusting an angularresolution of a LIDAR device by adjusting a pulse rate of the LIDARdevice.

FIG. 5B is a flowchart of a process for driving a LIDAR device at apulse rate exceeding a maximum sustained pulse rate for thermally stableoperation.

FIG. 6A symbolically illustrates a LIDAR device scanning across anexample obstacle-filled environmental scene with constant time delaysbetween successive measurement points where the time delays areexaggerated for illustrative purposes.

FIG. 6B is a timing diagram of the transmitted and received pulses forthe exaggerated symbolic illustration of FIG. 6A.

FIG. 6C symbolically illustrates a LIDAR device scanning across theexample obstacle-filled environmental scene shown with adaptivelyadjusted time delays between successive measurement points to provideenhanced angular resolution in the vicinity of the environmentalobstacle where the time delays are exaggerated for illustrativepurposes.

FIG. 6D is a timing diagram of the transmitted and received pulses forthe exaggerated symbolic illustration of FIG. 6C.

FIG. 7A is a flowchart of a process for identifying regions of aLIDAR-indicated point cloud to examine with enhanced angular resolutionby detecting edge(s).

FIG. 7B is a flowchart of another process for identifying regions of aLIDAR-indicated point cloud to examine with enhanced angular resolutionby detecting moving objects.

FIG. 7C is a flowchart of another process for identifying regions of aLIDAR-indicated point cloud to examine with enhanced angular resolutionby comparison with a baseline point map

FIG. 7D is a flowchart of another process for identifying regions of aLIDAR-indicated point cloud to examine with enhanced angular resolutionby detecting features beyond a threshold distance.

FIG. 7E is a flowchart of another process for identifying regions of aLIDAR-indicated point cloud to examine with enhanced angular resolutionby detecting a discontinuity during a scan corresponding to an objectedge.

FIG. 7F is a flowchart of another process for identifying regions of aLIDAR-indicated point cloud to examine with enhanced angular resolutionby identifying features arranged with high spatial or temporalfrequency.

FIG. 7G is a flowchart of another process for identifying regions of aLIDAR-indicated point cloud to examine with enhanced angular resolutionby detecting features that lack sufficient detail for accuratecategorization or identification.

FIG. 8 is a flowchart of a process for adaptively adjusting an angularresolution of a LIDAR device by adjusting a slew rate of the LIDARdevice.

FIG. 9A symbolically illustrates a LIDAR device scanning across anexample obstacle-filled environmental scene with adaptively adjustedslew rate of the LIDAR device to provide enhanced angular resolution inthe vicinity of the environmental obstacle where the time-scale isexaggerated for illustrative purposes.

FIG. 9B is a timing diagram of the transmitted and received pulses forthe exaggerated symbolic illustration of FIG. 9A.

FIG. 10 is a flowchart of a process for generating a point cloud withvariable angular resolution by use of a second LIDAR device withenhanced angular resolution.

FIG. 11 is a block diagram of an obstacle detection system with twoLIDAR devices where one provides enhanced angular resolution scans ofregions identified according to scans from the other.

FIG. 12 depicts a computer-readable medium configured according to anexample embodiment.

DETAILED DESCRIPTION

Example embodiments relate to an autonomous vehicle, such as adriverless automobile, that includes a light detection and ranging(LIDAR) sensor for actively detecting reflective features in theenvironment surrounding the vehicle. A controller analyzes informationfrom the LIDAR sensor to identify the surroundings of the vehicle anddetermines how to direct the propulsion systems of the vehicle to affecta navigation path that substantially avoids obstacles indicated by theinformation from the LIDAR sensor.

In some embodiments of the present disclosure, the angular resolution ofthe LIDAR sensor is adjusted to provide enhanced angular resolutionscanning in identified regions of an environmental scene. In someexamples, the LIDAR device operates by scanning a pulsing laser across ascanning zone, and measuring the time delays until reception ofcorresponding reflected pulses. The orientation of the LIDAR device foreach pulse is combined with the measured time delay to determine theposition of the environmental feature responsible for reflecting thepulse. Combining many such points provides a three-dimensional pointcloud representing the environmental scene surrounding the vehicle. Thepulsing laser can be scanned by directing the laser with optical beamsteering optics, such as a rotating angled mirror that directs a fixedlaser source according to the angle of the mirror. Some embodiments ofthe present disclosure provide for adjusting the angular resolution ofsuch a scanning LIDAR system by adjusting one or both of: (1) a pulserate of the pulsing laser, or (2) an angular rate of change of the beamsteering optics. Modifying one or both of the angular rate of change orthe pulse rate modifies the amount of angular change between eachsuccessive pulse emitted from the LIDAR sensor, and thereby modifies theangular resolution of the point cloud output from the LIDAR system. Insome examples, a second LIDAR device provides high resolution scanningof regions identified according to point cloud information from a firstLIDAR device providing wide-view scanning resolutions.

Regions to scan with enhanced angular resolution can be automaticallyidentified by analyzing the point cloud information from one or moreprevious scans and/or the current scan. In some examples, data from oneor more previously detected point maps are analyzed to identify regionsof the scene that include edges of perceived objects. The next scan canthen selectively provide enhanced angular resolution near the edges soas to better define the boundaries of the perceived feature whilemaintaining standard resolution, or even decreased resolution, in theregions between the edges. Additionally or alternatively, an enhancedresolution region can be indicated by identifying a feature in motion, afeature not present in a baseline map of the scene, or a distantfeature. Furthermore, in some examples, enhanced resolution scanning canbe initiated before completing a full scan on the basis of a partialpoint map that includes a discontinuity indicative of an edge of areflective environmental feature.

The spatial resolution of a LIDAR-generated 3-D point map depends on thephysical separation between points, which is a function of both thedistance to the points and the angular separation between the points,with respect to the LIDAR. For example, smaller angular separationbetween measured points provides higher spatial resolution for a givendistance, and vice versa. Similarly, smaller distances result in higherspatial resolution for a given angular separation, and vice versa. Theangular separation between points is sometimes referred to as the“density” of points, whereby higher density generally corresponds tohigher spatial resolution, and vice versa.

Some embodiments of the present disclosure also provide for achievingenhanced angular resolution in a LIDAR system by driving a pulsing laserat a pulse rate that exceeds a maximum sustained pulse rate associatedwith thermally stable device operation. The maximum sustained thermallystable pulse rate is determined according to thermal device behaviorsuch as heat dissipation characteristics, heat generation in lasercomponents, temperature-dependent behavior of associated opticalcomponents, etc. Lasers employed in LIDAR systems therefore have adevice-specific maximum sustained pulse rate that allows the device tocontinuously operate without experiencing adverse thermal effects. Insome examples, however, the maximum sustained pulse rate can betemporarily exceeded if followed by a corresponding decrease in pulserate such that the average pulse rate of the laser system does notexceed the maximum sustained rate.

For a conventional LIDAR system that does not provide adaptive angularresolution adjustments, and instead provides equally spaced samplepoints across the scanning zone, the maximum theoretical angularresolution of the system is determined by the refresh rate (number ofcomplete scans to be completed per second), the total solid anglescanned during each complete scan, and the maximum sustained pulse rate.In such an arrangement, the minimum theoretical angular separationbetween equally spaced points is given by:

Ω_(theor)=Ω_(tot)/[total pulses per scan],

Where Ω_(tot) is the total solid angle scanned during each scan, and thetotal pulses per scan is a function of the maximum sustained pulse rateand the refresh rate. For example, the total pulses per scan can begiven by f_(thermal)/f_(refresh), where f_(thermal) is the maximumsustained pulse rate, and f_(fresh) is the refresh rate of the LIDARsystem.

However, in contrast to conventional systems, some embodiments of thepresent disclosure allow for adaptively adjusting the angular resolutionacross a scanning zone to achieve enhanced angular resolutions inidentified regions of the scene. For example, the enhanced angularresolutions may exceed the theoretical maximums of conventional LIDARsystems.

Generally, the refresh rate for the LIDAR is set to providehigh-resolution, real-time 3-D point maps on a time scale that isrelevant to navigation decisions, such as adjustments to propulsionsystems of the autonomous vehicle in real time. Thus, in some examples,the refresh rate may be dependent on the vehicle's rate of speed. Forexample, the refresh rate may be higher at high rates of speeds, becauseat high speeds potential obstacles (and the need to maneuver aroundthem) tend to develop on relatively short time scales for a potentialobstacle at a fixed distance. On the other hand, the refresh rate may belower at low rates of speed, because at low speeds potential obstacles(and the need to maneuver around them) tend to develop on relativelygreater time scales. There may be other factors and/or considerations,as well as other applications of scanning LIDARs, which make rapidgeneration of high-resolution 3-D point maps in real time necessary ordesirable. Moreover, rapid generation of high-resolution 3-D point mapsmay be important or desirable for reasons other than safety.

Some aspects of the example methods described herein may be carried outin whole or in part by an autonomous vehicle or components thereof.However, some example methods may also be carried out in whole or inpart by a system or systems that are remote from an autonomous vehicle.For instance, an example method could be carried out in part or in fullby a server system, which receives information from sensors (e.g., rawsensor data and/or information derived therefrom) of an autonomousvehicle. Other examples are also possible.

Example systems within the scope of the present disclosure will now bedescribed in greater detail. An example system may be implemented in, ormay take the form of, an automobile. However, an example system may alsobe implemented in or take the form of other vehicles, such as cars,trucks, motorcycles, buses, boats, airplanes, helicopters, lawn mowers,earth movers, boats, snowmobiles, aircraft, recreational vehicles,amusement park vehicles, farm equipment, construction equipment, trams,golf carts, trains, and trolleys. Other vehicles are possible as well.

FIG. 1 is a functional block diagram illustrating a vehicle 100according to an example embodiment. The vehicle 100 is configured tooperate fully or partially in an autonomous mode, and thus may bereferred to as an “autonomous vehicle.” For example, a computer system112 can control the vehicle 100 while in an autonomous mode via controlinstructions to a control system 106 for the vehicle 100. The computersystem 112 can receive information from one or more sensor systems 104,and base one or more control processes (such as the setting a heading soas to avoid a detected obstacle) upon the received information in anautomated fashion.

The autonomous vehicle 100 can be fully autonomous or partiallyautonomous. In a partially autonomous vehicle some functions canoptionally be manually controlled (e.g., by a driver) some or all of thetime. Further, a partially autonomous vehicle can be configured toswitch between a fully-manual operation mode and a partially-autonomousand/or a fully-autonomous operation mode.

The vehicle 100 includes a propulsion system 102, a sensor system 104, acontrol system 106, one or more peripherals 108, a power supply 110, acomputer system 112, and a user interface 116. The vehicle 100 mayinclude more or fewer subsystems and each subsystem can optionallyinclude multiple components. Further, each of the subsystems andcomponents of vehicle 100 can be interconnected and/or in communication.Thus, one or more of the functions of the vehicle 100 described hereincan optionally be divided between additional functional or physicalcomponents, or combined into fewer functional or physical components. Insome further examples, additional functional and/or physical componentsmay be added to the examples illustrated by FIG. 1.

The propulsion system 102 can include components operable to providepowered motion to the vehicle 100. In some embodiments the propulsionsystem 102 includes an engine/motor 118, an energy source 119, atransmission 120, and wheels/tires 121. The engine/motor 118 convertsenergy source 119 to mechanical energy. In some embodiments, thepropulsion system 102 can optionally include one or both of enginesand/or motors. For example, a gas-electric hybrid vehicle can includeboth a gasoline/diesel engine and an electric motor.

The energy source 119 represents a source of energy, such as electricaland/or chemical energy, that may, in full or in part, power theengine/motor 118. That is, the engine/motor 118 can be configured toconvert the energy source 119 to mechanical energy to operate thetransmission. In some embodiments, the energy source 119 can includegasoline, diesel, other petroleum-based fuels, propane, other compressedgas-based fuels, ethanol, solar panels, batteries, capacitors,flywheels, regenerative braking systems, and/or other sources ofelectrical power, etc. The energy source 119 can also provide energy forother systems of the vehicle 100.

The transmission 120 includes appropriate gears and/or mechanicalelements suitable to convey the mechanical power from the engine/motor118 to the wheels/tires 121. In some embodiments, the transmission 120includes a gearbox, a clutch, a differential, a drive shaft, and/oraxle(s), etc.

The wheels/tires 121 are arranged to stably support the vehicle 100while providing frictional traction with a surface, such as a road, uponwhich the vehicle 100 moves. Accordingly, the wheels/tires 121 areconfigured and arranged according to the nature of the vehicle 100. Forexample, the wheels/tires can be arranged as a unicycle, bicycle,motorcycle, tricycle, or car/truck four-wheel format. Other wheel/tiregeometries are possible, such as those including six or more wheels. Anycombination of the wheels/tires 121 of vehicle 100 may be operable torotate differentially with respect to other wheels/tires 121. Thewheels/tires 121 can optionally include at least one wheel that isrigidly attached to the transmission 120 and at least one tire coupledto a rim of a corresponding wheel that makes contact with a drivingsurface. The wheels/tires 121 may include any combination of metal andrubber, and/or other materials or combination of materials.

The sensor system 104 generally includes one or more sensors configuredto detect information about the environment surrounding the vehicle 100.For example, the sensor system 104 can include a Global PositioningSystem (GPS) 122, an inertial measurement unit (IMU) 124, a RADAR unit126, a laser rangefinder/LIDAR unit 128, a camera 130, and/or amicrophone 131. The sensor system 104 could also include sensorsconfigured to monitor internal systems of the vehicle 100 (e.g., O₂monitor, fuel gauge, engine oil temperature, wheel speed sensors, etc.).One or more of the sensors included in sensor system 104 could beconfigured to be actuated separately and/or collectively in order tomodify a position and/or an orientation of the one or more sensors.

The GPS 122 is a sensor configured to estimate a geographic location ofthe vehicle 100. To this end, GPS 122 can include a transceiver operableto provide information regarding the position of the vehicle 100 withrespect to the Earth.

The IMU 124 can include any combination of sensors (e.g., accelerometersand gyroscopes) configured to sense position and orientation changes ofthe vehicle 100 based on inertial acceleration.

The RADAR unit 126 can represent a system that utilizes radio signals tosense objects within the local environment of the vehicle 100. In someembodiments, in addition to sensing the objects, the RADAR unit 126and/or the computer system 112 can additionally be configured to sensethe speed and/or heading of the objects.

Similarly, the laser rangefinder or LIDAR unit 128 can be any sensorconfigured to sense objects in the environment in which the vehicle 100is located using lasers. The laser rangefinder/LIDAR unit 128 caninclude one or more laser sources, a laser scanner, and one or moredetectors, among other system components. The laser rangefinder/LIDARunit 128 can be configured to operate in a coherent (e.g., usingheterodyne detection) or an incoherent detection mode.

The camera 130 can include one or more devices configured to capture aplurality of images of the environment surrounding the vehicle 100. Thecamera 130 can be a still camera or a video camera. In some embodiments,the camera 130 can be mechanically movable such as by rotating and/ortilting a platform to which the camera is mounted. As such, a controlprocess of vehicle 100 may be implemented to control the movement ofcamera 130.

The sensor system 104 can also include a microphone 131. The microphone131 can be configured to capture sound from the environment surroundingvehicle 100. In some cases, multiple microphones can be arranged as amicrophone array, or possibly as multiple microphone arrays.

The control system 106 is configured to control operation(s) regulatingacceleration of the vehicle 100 and its components. To effectacceleration, the control system 106 includes a steering unit 132,throttle 134, brake unit 136, a sensor fusion algorithm 138, a computervision system 140, a navigation/pathing system 142, and/or an obstacleavoidance system 144, etc.

The steering unit 132 is operable to adjust the heading of vehicle 100.For example, the steering unit can adjust the axis (or axes) of one ormore of the wheels/tires 121 so as to effect turning of the vehicle. Thethrottle 134 is configured to control, for instance, the operating speedof the engine/motor 118 and, in turn, adjust forward acceleration of thevehicle 100 via the transmission 120 and wheels/tires 121. The brakeunit 136 decelerates the vehicle 100. The brake unit 136 can usefriction to slow the wheels/tires 121. In some embodiments, the brakeunit 136 inductively decelerates the wheels/tires 121 by a regenerativebraking process to convert kinetic energy of the wheels/tires 121 toelectric current.

The sensor fusion algorithm 138 is an algorithm (or a computer programproduct storing an algorithm) configured to accept data from the sensorsystem 104 as an input. The data may include, for example, datarepresenting information sensed at the sensors of the sensor system 104.The sensor fusion algorithm 138 can include, for example, a Kalmanfilter, Bayesian network, etc. The sensor fusion algorithm 138 providesassessments regarding the environment surrounding the vehicle based onthe data from sensor system 104. In some embodiments, the assessmentscan include evaluations of individual objects and/or features in theenvironment surrounding vehicle 100, evaluations of particularsituations, and/or evaluations of possible interference between thevehicle 100 and features in the environment (e.g., such as predictingcollisions and/or impacts) based on the particular situations.

The computer vision system 140 can process and analyze images capturedby camera 130 to identify objects and/or features in the environmentsurrounding vehicle 100. The detected features/objects can includetraffic signals, road way boundaries, other vehicles, pedestrians,and/or obstacles, etc. The computer vision system 140 can optionallyemploy an object recognition algorithm, a Structure From Motion (SFM)algorithm, video tracking, and/or available computer vision techniquesto effect categorization and/or identification of detectedfeatures/objects. In some embodiments, the computer vision system 140can be additionally configured to map the environment, track perceivedobjects, estimate the speed of objects, etc.

The navigation and pathing system 142 is configured to determine adriving path for the vehicle 100. For example, the navigation andpathing system 142 can determine a series of speeds and directionalheadings to effect movement of the vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing thevehicle along a roadway-based path leading to an ultimate destination,which can be set according to user inputs via the user interface 116,for example. The navigation and pathing system 142 can additionally beconfigured to update the driving path dynamically while the vehicle 100is in operation on the basis of perceived obstacles, traffic patterns,weather/road conditions, etc. In some embodiments, the navigation andpathing system 142 can be configured to incorporate data from the sensorfusion algorithm 138, the GPS 122, and one or more predetermined maps soas to determine the driving path for vehicle 100.

The obstacle avoidance system 144 can represent a control systemconfigured to identify, evaluate, and avoid or otherwise negotiatepotential obstacles in the environment surrounding the vehicle 100. Forexample, the obstacle avoidance system 144 can effect changes in thenavigation of the vehicle by operating one or more subsystems in thecontrol system 106 to undertake swerving maneuvers, turning maneuvers,braking maneuvers, etc. In some embodiments, the obstacle avoidancesystem 144 is configured to automatically determine feasible(“available”) obstacle avoidance maneuvers on the basis of surroundingtraffic patterns, road conditions, etc. For example, the obstacleavoidance system 144 can be configured such that a swerving maneuver isnot undertaken when other sensor systems detect vehicles, constructionbarriers, other obstacles, etc. in the region adjacent the vehicle thatwould be swerved into. In some embodiments, the obstacle avoidancesystem 144 can automatically select the maneuver that is both availableand maximizes safety of occupants of the vehicle. For example, theobstacle avoidance system 144 can select an avoidance maneuver predictedto cause the least amount of acceleration in a passenger cabin of thevehicle 100.

The vehicle 100 also includes peripherals 108 configured to allowinteraction between the vehicle 100 and external sensors, othervehicles, other computer systems, and/or a user, such as an occupant ofthe vehicle 100. For example, the peripherals 108 for receivinginformation from occupants, external systems, etc. can include awireless communication system 146, a touchscreen 148, a microphone 150,and/or a speaker 152.

In some embodiments, the peripherals 108 function to receive inputs fora user of the vehicle 100 to interact with the user interface 116. Tothis end, the touchscreen 148 can both provide information to a user ofvehicle 100, and convey information from the user indicated via thetouchscreen 148 to the user interface 116. The touchscreen 148 can beconfigured to sense both touch positions and touch gestures from auser's finger (or stylus, etc.) via capacitive sensing, resistancesensing, optical sensing, a surface acoustic wave process, etc. Thetouchscreen 148 can be capable of sensing finger movement in a directionparallel or planar to the touchscreen surface, in a direction normal tothe touchscreen surface, or both, and may also be capable of sensing alevel of pressure applied to the touchscreen surface. An occupant of thevehicle 100 can also utilize a voice command interface. For example, themicrophone 150 can be configured to receive audio (e.g., a voice commandor other audio input) from a user of the vehicle 100. Similarly, thespeakers 152 can be configured to output audio to the user of thevehicle 100.

In some embodiments, the peripherals 108 function to allow communicationbetween the vehicle 100 and external systems, such as devices, sensors,other vehicles, etc. within its surrounding environment and/orcontrollers, servers, etc., physically located far from the vehicle thatprovide useful information regarding the vehicle's surroundings, such astraffic information, weather information, etc. For example, the wirelesscommunication system 146 can wirelessly communicate with one or moredevices directly or via a communication network. The wirelesscommunication system 146 can optionally use 3G cellular communication,such as CDMA, EVDO, GSM/GPRS, and/or 4G cellular communication, such asWiMAX or LTE. Additionally or alternatively, wireless communicationsystem 146 can communicate with a wireless local area network (WLAN),for example, using WiFi. In some embodiments, wireless communicationsystem 146 could communicate directly with a device, for example, usingan infrared link, Bluetooth, and/or ZigBee. The wireless communicationsystem 146 can include one or more dedicated short range communication(DSRC) devices that can include public and/or private datacommunications between vehicles and/or roadside stations. Other wirelessprotocols for sending and receiving information embedded in signals,such as various vehicular communication systems, can also be employed bythe wireless communication system 146 within the context of the presentdisclosure.

As noted above, the power supply 110 can provide power to components ofvehicle 100, such as electronics in the peripherals 108, computer system112, sensor system 104, etc. The power supply 110 can include arechargeable lithium-ion or lead-acid battery for storing anddischarging electrical energy to the various powered components, forexample. In some embodiments, one or more banks of batteries can beconfigured to provide electrical power. In some embodiments, the powersupply 110 and energy source 119 can be implemented together, as in someall-electric cars.

Many or all of the functions of vehicle 100 can be controlled viacomputer system 112 that receives inputs from the sensor system 104,peripherals 108, etc., and communicates appropriate control signals tothe propulsion system 102, control system 106, peripherals, etc. toeffect automatic operation of the vehicle 100 based on its surroundings.Computer system 112 includes at least one processor 113 (which caninclude at least one microprocessor) that executes instructions 115stored in a non-transitory computer readable medium, such as the datastorage 114. The computer system 112 may also represent a plurality ofcomputing devices that serve to control individual components orsubsystems of the vehicle 100 in a distributed fashion.

In some embodiments, data storage 114 contains instructions 115 (e.g.,program logic) executable by the processor 113 to execute variousfunctions of vehicle 100, including those described above in connectionwith FIG. 1. Data storage 114 may contain additional instructions aswell, including instructions to transmit data to, receive data from,interact with, and/or control one or more of the propulsion system 102,the sensor system 104, the control system 106, and the peripherals 108.

In addition to the instructions 115, the data storage 114 may store datasuch as roadway maps, path information, among other information. Suchinformation may be used by vehicle 100 and computer system 112 duringoperation of the vehicle 100 in the autonomous, semi-autonomous, and/ormanual modes to select available roadways to an ultimate destination,interpret information from the sensor system 104, etc.

The vehicle 100, and associated computer system 112, providesinformation to and/or receives input from, a user of vehicle 100, suchas an occupant in a passenger cabin of the vehicle 100. The userinterface 116 can accordingly include one or more input/output deviceswithin the set of peripherals 108, such as the wireless communicationsystem 146, the touchscreen 148, the microphone 150, and/or the speaker152 to allow communication between the computer system 112 and a vehicleoccupant.

The computer system 112 controls the operation of the vehicle 100 basedon inputs received from various subsystems indicating vehicle and/orenvironmental conditions (e.g., propulsion system 102, sensor system104, and/or control system 106), as well as inputs from the userinterface 116, indicating user preferences. For example, the computersystem 112 can utilize input from the control system 106 to control thesteering unit 132 to avoid an obstacle detected by the sensor system 104and the obstacle avoidance system 144. The computer system 112 can beconfigured to control many aspects of the vehicle 100 and itssubsystems. Generally, however, provisions are made for manuallyoverriding automated controller-driven operation, such as in the eventof an emergency, or merely in response to a user-activated override,etc.

The components of vehicle 100 described herein can be configured to workin an interconnected fashion with other components within or outsidetheir respective systems. For example, the camera 130 can capture aplurality of images that represent information about an environment ofthe vehicle 100 while operating in an autonomous mode. The environmentmay include other vehicles, traffic lights, traffic signs, road markers,pedestrians, etc. The computer vision system 140 can categorize and/orrecognize various aspects in the environment in concert with the sensorfusion algorithm 138, the computer system 112, etc. based on objectrecognition models pre-stored in data storage 114, and/or by othertechniques.

Although the vehicle 100 is described and shown in FIG. 1 as havingvarious components of vehicle 100, e.g., wireless communication system146, computer system 112, data storage 114, and user interface 116,integrated into the vehicle 100, one or more of these components canoptionally be mounted or associated separately from the vehicle 100. Forexample, data storage 114 can exist, in part or in full, separate fromthe vehicle 100, such as in a cloud-based server, for example. Thus, oneor more of the functional elements of the vehicle 100 can be implementedin the form of device elements located separately or together. Thefunctional device elements that make up vehicle 100 can generally becommunicatively coupled together in a wired and/or wireless fashion.

FIG. 2 shows an example vehicle 200 that can include all or most of thefunctions described in connection with vehicle 100 in reference toFIG. 1. Although vehicle 200 is illustrated in FIG. 2 as a four-wheelsedan-type car for illustrative purposes, the present disclosure is notso limited. For instance, the vehicle 200 can represent a truck, a van,a semi-trailer truck, a motorcycle, a golf cart, an off-road vehicle, ora farm vehicle, etc.

The example vehicle 200 includes a sensor unit 202, a wirelesscommunication system 204, a LIDAR unit 206, a laser rangefinder unit208, and a camera 210. Furthermore, the example vehicle 200 can includeany of the components described in connection with vehicle 100 of FIG.1.

The sensor unit 202 is mounted atop the vehicle 200 and includes one ormore sensors configured to detect information about an environmentsurrounding the vehicle 200, and output indications of the information.For example, sensor unit 202 can include any combination of cameras,RADARs, LIDARs, range finders, and acoustic sensors. The sensor unit 202can include one or more movable mounts that could be operable to adjustthe orientation of one or more sensors in the sensor unit 202. In oneembodiment, the movable mount could include a rotating platform thatcould scan sensors so as to obtain information from each directionaround the vehicle 200. In another embodiment, the movable mount of thesensor unit 202 could be moveable in a scanning fashion within aparticular range of angles and/or azimuths. The sensor unit 202 could bemounted atop the roof of a car, for instance, however other mountinglocations are possible. Additionally, the sensors of sensor unit 202could be distributed in different locations and need not be collocatedin a single location. Some possible sensor types and mounting locationsinclude LIDAR unit 206 and laser rangefinder unit 208. Furthermore, eachsensor of sensor unit 202 could be configured to be moved or scannedindependently of other sensors of sensor unit 202.

The wireless communication system 204 could be located on a roof of thevehicle 200 as depicted in FIG. 2. Alternatively, the wirelesscommunication system 204 could be located, fully or in part, elsewhere.The wireless communication system 204 may include wireless transmittersand receivers that could be configured to communicate with devicesexternal or internal to the vehicle 200. Specifically, the wirelesscommunication system 204 could include transceivers configured tocommunicate with other vehicles and/or computing devices, for instance,in a vehicular communication system or a roadway station. Examples ofsuch vehicular communication systems include dedicated short rangecommunications (DSRC), radio frequency identification (RFID), and otherproposed communication standards directed towards intelligent transportsystems.

The camera 210 can be a photo-sensitive instrument, such as a stillcamera, a video camera, etc., that is configured to capture a pluralityof images of the environment of the vehicle 200. To this end, the camera210 can be configured to detect visible light, and can additionally oralternatively be configured to detect light from other portions of thespectrum, such as infrared or ultraviolet light. The camera 210 can be atwo-dimensional detector, and can optionally have a three-dimensionalspatial range of sensitivity. In some embodiments, the camera 210 caninclude, for example, a range detector configured to generate atwo-dimensional image indicating distance from the camera 210 to anumber of points in the environment. To this end, the camera 210 may useone or more range detecting techniques.

For example, the camera 210 can provide range information by using astructured light technique in which the vehicle 200 illuminates anobject in the environment with a predetermined light pattern, such as agrid or checkerboard pattern and uses the camera 210 to detect areflection of the predetermined light pattern from environmentalsurroundings. Based on distortions in the reflected light pattern, thevehicle 200 can determine the distance to the points on the object. Thepredetermined light pattern may comprise infrared light, or radiation atother suitable wavelengths for such measurements.

The camera 210 can be mounted inside a front windshield of the vehicle200. Specifically, the camera 210 can be situated to capture images froma forward-looking view with respect to the orientation of the vehicle200. Other mounting locations and viewing angles of camera 210 can alsobe used, either inside or outside the vehicle 200.

The camera 210 can have associated optics operable to provide anadjustable field of view. Further, the camera 210 can be mounted tovehicle 200 with a movable mount to vary a pointing angle of the camera210, such as a via a pan/tilt mechanism.

FIG. 3A provides an example depiction of a light detection and ranging(LIDAR) device 302 including beam steering optics 304. A laser beam 306is directed to the beam steering optics 304. In the example illustratedin FIG. 3A, the beam steering optics 304 is a rotating angled mirrorthat directs the initially downward facing laser beam 306 to sweepacross a scanning zone. As described herein, the beam steering optics304, which can generally be implemented as a combination of lenses,mirrors, and/or apertures configured to direct the laser beam to sweepacross a scanning zone, are interchangeably described as the rotatingangled mirror 304. The rotating angled mirror 304 rotates about an axissubstantially parallel, and roughly in line with, the initial downwardpath of the laser beam 306. The rotating angled mirror 304 rotates inthe direction indicated by the reference arrow 308 in FIG. 3A.

Although rangefinder 302 is depicted as having (approximately) a 180degree range of rotation for the scanning zone of the laser beam 306 viathe rotating angled mirror 304, this is for purposes of example andexplanation only, as the present disclosure is not so limited. Indeed,as explained above, LIDAR 302 can be configured to have viewing angle(e.g., angular range of available orientations during each sweep),including viewing angles up to and including 360 degrees. Further,although LIDAR 302 is depicted with the single laser beam 306 and asingle mirror 304, this is for purposes of example and explanation only,as the present disclosure is not so limited. Indeed, as explained above,LIDAR 302 can include multiple laser beams operating simultaneously orsequentially to provide greater sampling coverage of the surroundingenvironment. The LIDAR 302 also includes, or works in concert with,additional optical sensors (not shown) configured to detect thereflection of laser beam 306 from features/objects in the surroundingenvironment with sufficient temporal sensitivity to determine distancesto the reflective features. For example, with reference to the vehicle200 in FIG. 2, such optical sensors can optionally be co-located withthe top-mounted sensors 204 on the autonomous vehicle 200.

FIG. 3B symbolically illustrates the LIDAR device 302 scanning across anobstacle-filled environmental scene. The example vehicular environmentdepicted in FIG. 3B includes a car 310 and a tree 312. In operation,LIDAR 302 rotates according to motion reference arrow 308 with angularvelocity w. While rotating, the LIDAR 302 regularly (e.g., periodically)emits laser beams, such as the laser beam 306. Reflections from theemitted laser beams by objects in the environment, such as vehicle 310and tree 312, are then received by suitable sensors. Preciselytime-stamping the receipt of the reflected signals allows forassociating each reflected signal (if any is received at all) with themost recently emitted laser pulse, and measuring the time delay betweenemission of the laser pulse and reception of the reflected light. Thetime delay provides an estimate of the distance to the reflectivefeature by scaling according to the speed of light in the interveningatmosphere. Combining the distance information for each reflected signalwith the orientation of the LIDAR device 302 for the respective pulseemission allows for determining a position of the reflective feature inthree-dimensions. For illustrative purposes, the environmental scene inFIG. 3B is described in the two-dimensional x-y plane in connection witha single sweep of the LIDAR device 302 that estimates positions to aseries of points located in the x-y plane. However, it is noted that amore complete three-dimensional sampling is provided by either adjustingthe beam steering optics 304 to direct the laser beam 306 up or downfrom the x-y plane on its next sweep of the scene or by providingadditional lasers and associated beam steering optics dedicated tosampling point locations in planes above and below the x-y plane shownin FIG. 3B, or combinations of these.

FIG. 3C symbolically illustrates a point cloud corresponding to theobstacle-filled environmental scene of FIG. 3B. Spatial-point data(represented by stars) are shown from a ground-plane (or aerial)perspective. Even though the individual points are not equally spatiallydistributed throughout the sampled environment, adjacent sampled pointsare roughly equally angularly spaced with respect to the LIDAR device302. A car spatial data 314 correspond to measured points on the surfaceof the car 310 with a line of sight to the LIDAR device 302. Similarly,a tree spatial data 316 correspond to measured points on the surface ofthe tree 312 visible from the LIDAR device 302. The absence of pointsbetween the car spatial data 314 and the tree spatial data 316 indicatesan absence of reflective features along the sampled line of sight pathsin the plane illustrated.

Each point in the example point cloud illustrated symbolically in FIG.3C can be referenced by an azimuth angle φ (e.g. orientation of theLIDAR device 302 while emitting the pulse corresponding to the point,which is determined by the orientation of the rotating angled mirror304) and a line-of-sight (LOS) distance (e.g., the distance indicated bythe time delay between pulse emission and reflected light reception).For emitted pulses that do not receive a reflected signal, the LOSdistance can optionally be set to the maximum distance sensitivity ofthe LIDAR device 302. The maximum distance sensitivity can be determinedaccording to the maximum time delay the associated optical sensors waitfor a return reflected signal following each pulse emission, which canitself be set according to the anticipated signal strength of areflected signal at a particular distance given ambient lightingconditions, intensity of the emitted pulse, predicted reflectivity ofenvironmental features, etc. In some examples, the maximum distance canbe approximately 60 meters, 80 meters, 100 meters, or 150 meters, butother examples are possible for particular configurations of the LIDARdevice 302 and associated optical sensors.

In some embodiments, the sensor fusion algorithm 138, computer visionsystem 140, and/or computer system 112, can interpret the car spatialdata 314 alone and/or in combination with additional sensor-indicatedinformation and/or memory-based pattern-matching point clouds and/orbaseline maps of the environment to categorize or identify the group ofpoints 314 as corresponding to the car 310. Similarly, the tree spatialdata 316 can identified as corresponding to the tree 310 in accordancewith a suitable object-detection technique. As described further herein,some embodiments of the present disclosure provide for identifying aregion of the point cloud for study with enhanced resolution scanningtechnique on the basis of the already-sampled spatial-points.

Further, as noted above, each spatial point can be associated with arespective laser from a set of lasers and a respective timestamp. Thatis, in an embodiment where the LIDAR 302 includes multiple lasers, eachrespective received spatial point can be associated with the particularlaser that was detected in accordance with the respective receivedspatial point. Additionally, each respective spatial point can beassociated with a respective timestamp (e.g., a time at which laser wasemitted or received). In this way, the received spatial points may beorganized, identified, or otherwise ordered on a spatial (laseridentification) and/or temporal (timestamp) basis. Such an ordering mayassist or improve an analysis of the spatial-point data by allowing fororganizing the spatial-point data into a meaningful order.

FIGS. 4A is a raw camera image of an example roadway scene 402approaching an intersection. FIG. 4B is a rendering of a LIDAR-indicatedpoint cloud 404 corresponding to the scene 402 pictured in FIG. 4A.FIGS. 4C is a raw camera image of another example roadway scene 406.FIG. 4D is a rendering of a LIDAR-indicated point cloud 408corresponding to the scene 406 pictured in FIG. 4C.

With reference to the vehicle 200 of FIG. 2, the camera images 402, 406of FIGS. 4A and 4C can be captured by the camera 210 of the vehicle 200,and the corresponding point cloud maps 404, 408 of FIGS. 4B and 4D canbe captured by the LIDAR sensor 206. As shown in the examples of FIG. 4Band 4D, a laser point cloud image may substantially or approximatelycorrespond to a raw camera image captured by a camera. Moreover, FIGS.4B and 4D show that LIDAR-indicated point cloud maps for the distinctenvironmental scenes 402, 406 (i.e., the images in FIGS. 4A and 4C)result in distinct point maps 404, 408 which correspond to theirrespective scenes. For clarity it is noted that each “line” depicted inFIGS. 4B and 4D generally corresponds to a series of spatial pointscollected by a particular, single, laser of LIDAR 206.

FIGS. 5A-5B, 7A-7G, 8 and 10 each present flowcharts describingprocesses employed separately or in combination in some embodiments ofthe present disclosure. The methods and processes described herein aregenerally described by way of example as being carried out by anautonomous vehicle, such as the autonomous vehicles 100, 200 describedabove in connection with FIGS. 1 and 2. For example, the processesdescribed herein can be carried out by a LIDAR sensor 128 and associatedoptical sensors (e.g., the sensors 202 on vehicle 200) mounted to anautonomous vehicle (e.g., the vehicle 200) in communication with acomputer system 112, sensor fusion algorithm module 138, and/or computervision system 140.

Furthermore, it is noted that the functionality described in connectionwith the flowcharts described herein can be implemented asspecial-function and/or configured general-function hardware modules,portions of program code executed by a processor (e.g., the processor113 in the computer system 112) for achieving specific logicalfunctions, determinations, and/or steps described in connection with theflowchart 500. Where used, program code can be stored on any type ofcomputer readable medium (e.g., computer readable storage medium ornon-transitory media, such as data storage 114 described above withrespect to computer system 112), for example, such as a storage deviceincluding a disk or hard drive. In addition, each block of the flowchart500 can represent circuitry that is wired to perform the specificlogical functions in the process. Unless specifically indicated,functions in the flowchart 500 can be executed out of order from thatshown or discussed, including substantially concurrent execution ofseparately described functions, or even in reverse order in someexamples, depending on the functionality involved, so long as theoverall functionality of the described method is maintained.Furthermore, similar combinations of hardware and/or software elementscan be employed to implement the methods described in connection withother flowcharts provided in the present disclosure, such as theadditional flowcharts shown in FIGS. 5A-5B, 7A-7G, 8, and 10.

For purposes of context, example, and explanation, an overview ofgeneral approaches to object detection is provided below in connectionwith an example LIDAR device. As noted above, example vehicle 100includes a LIDAR device 128. LIDAR 128 actively captures laser pointcloud images using one or more lasers. The laser point cloud includesmany points for each pulse emitted from the LIDAR device 128: reflectedsignals indicate actual locations of reflective objects, whereas failingto receive reflected signals indicate an absence of sufficientlyreflective objects within a particular distance along the line of sightof the laser. Depending on factors including the laser pulse rate, thescene refresh rate, the total solid angle sampled by each LIDAR device(or just the total solid angle of the scene, where only one

LIDAR device is used), the number of sample points in each point cloudcan be determined. Some embodiments can provide point clouds with asmany as 50,000 laser-indicated points, 80,000 laser-indicated points,100,000 laser-indicated points, etc. Generally, the number oflaser-indicated points in each point cloud is a tradeoff between angularresolution on the one hand, and refresh rate on the other hand. TheLIDAR device is driven to provide an angular resolution at asufficiently high refresh rate to be relevant to real time navigationaldecisions for an autonomous vehicle. Thus, the LIDAR 128 can beconfigured to capture one or more laser point clouds of theenvironmental scene at predetermined time intervals, such as 100milliseconds (for a refresh rate of 10 frames per second), 33milliseconds (for a refresh rate of 30 frames per second), 1millisecond, 1 second, etc.

Data storage 114 of computer system 112 of vehicle 100 can storeobject-detector software, code, or other program instructions. Suchobject-detector software can include, or be part of, one or more of thecontrol systems 106 described above, including the sensor fusionalgorithm 138, computer vision system 140, and/or obstacle avoidancesystem 144. The object detector may be any configuration of softwareand/or hardware configured to perceive features in the environmentalscene by categorizing and/or identifying objects based on the laserpoint clouds captured by the LIDAR 128 and/or based on one or more ofthe sensors in sensor system 104. As a laser point cloud is captured viaLIDAR 128, data indicative of the captured point cloud is communicatedto the object detector, which analyzes the data to determine whetherthere is an object present in the laser point cloud. Objects indicatedby the point cloud may be, for example, a vehicle, a pedestrian, a roadsign, a traffic light, a traffic cone, etc.

To determine whether an object is present in a laser point cloud image,the object detector software and/or module can associate arrangements oflaser-indicated points with patterns matching objects, environmentalfeatures, and/or categories of objects or features. The object detectorcan be pre-loaded (or dynamically instructed) to associate arrangementsaccording to one or more parameters corresponding to physicalobjects/features in the environment surrounding the vehicle 100. Forexample, the object detector can be pre-loaded with informationindicating a typical height of a pedestrian, a length of a typicalautomobile, confidence thresholds for classifying suspected objects,etc.

When the object detector identifies an object in point cloud, the objectdetector can define a bounding box encompassing the object that. Forexample, the bounding box can correspond to a predicted exterior surfaceof the point cloud indicated object. Of course, the bounding “box” cangenerally take the form of a multi-sided closed shape defining thepredicted outer boundaries of the object.

For each captured point cloud, positions of perceived objects and theircorresponding boundary definitions are associated with a frame number orframe time. Thus, similarly shaped objects appearing in roughly similarlocations in successive scans of the scene can be associated with oneanother to track objects in time. For perceived objects appearing inmultiple point cloud frames (e.g., complete scans of the scanning zone),the object can be associated, for each frame on which the objectappears, with a distinct bounding shape defining the dimensional extentof the perceived object.

Perceived objects can be tracked as the vehicle 100 travels through itssurrounding environment and/or as objects move with respect to thevehicle so as to pass through the scanning zone of the LIDAR 128.Combining two or more successively captured point clouds can therebyallow for determining translation information for detected objects.Future position predictions can be made for objects with characterizedmotion profiles, such as by observing acceleration and/or velocity ofobjects such as cars moving along the roadway with the vehicle 100 topredict the location of the object during a subsequent scan. In someembodiments, objects moving through the air are assumed to move along atrajectory influenced by the force of gravity.

To assist in providing object recognition, the vehicle 100 can also bein communication with an object-identification server (e.g., via thewireless communication system 146). The object-identification server canverify and/or classify objects detected by vehicle 100 using the objectdetector. Moreover, the object-identification server can facilitateoptimization of one or more of the parameters used by the objectdetector to detect objects in the captured laser point cloud based onaccumulated data from other similar systems, local conditions. In oneembodiment, vehicle 100 can communicate the object boundaries, and theircorresponding object parameters, to the object identification server forverification that the perceived objects are correctly identified, suchas indicated by an evaluation for statistical likelihood of correctidentification.

In some embodiments, a single laser in the LIDAR device (e.g., the LIDARdevice 302 discussed in connection with FIGS. 3A-3C) can have a scanningrange of approximately 150 meters distance, a thirty degree vertical(“altitude”) field of view, and approximately a thirty degree horizontal(“azimuth”) field of view. Additional lasers included in the LIDARdevice can have complementary ranges and fields of view as well so as toprovide sufficient coverage of an environmental scene to informnavigational determinations.

FIG. 5A is a flowchart 500 of a process for adaptively adjusting anangular resolution of a LIDAR device by adjusting a pulse rate of theLIDAR device. A LIDAR sensor is scanned through a scanning zone (502).In some embodiments, a LIDAR sensor can be similar to the LIDAR device302 described in connection with FIGS. 3A-3C and can be scanned througha scanning zone by manipulating beam steering optics (e.g., the rotatingangled mirror 304) to direct a pulsing laser to sweep across a scene.The scanning zone scanned by the LIDAR sensor is then the complete scenescanned by the pulsing laser. Of course, as described above, scanningthe scanning zone can be carried out by multiple individual LIDARdevices each scanning respective sections of the entire scanning zone,and which sections can optionally be partially overlapping.

During and/or following the scan of the scanning zone (502), data fromthe LIDAR sensor is analyzed to generate a three-dimensional (“3-D”)point cloud of positions of detected reflective points definingreflective features in the environment surrounding the vehicle. Forexample, data from the LIDAR sensor can include correlated lists oforientation of the LIDAR device (e.g., altitude and azimuth angles), toindicate direction to each point, and time delay between emission andreception, to indicate distance to each point.

The generated point cloud is analyzed to identify region(s) of theenvironment surrounding the vehicle for study at enhanced angularresolution (506). Example techniques for identifying the region(s) forenhanced resolution study are discussed further below in connection withthe flowcharts in FIGS. 7A-7G. The region(s) can be identified by anautomated process configured to receive point cloud data, analyze one ormore frames of point cloud data, and identify regions of the environmentindicated by the point cloud data that warrant examination at enhancedangular resolution. Such an automated process can be implemented viahardware modules and/or software components executed via the computersystem 112 and processor 114 to provide functions such as patternrecognition, object detection, etc. to analyze the 3-D point clouds.

While scanning the identified region(s), the LIDAR sensor is driven withan increased pulse rate (508) so as to increase the density of samplepoints in the identified region(s) and thereby increase the localangular resolution of the point cloud. For example, with reference tothe LIDAR device 302, the rate of angular sweeping provided by the beamsteering optics 304 can be maintained at a constant rate while the pulserate of the laser source is increased such that the time delay betweensuccessively emitted pulses, and similarly the amount of angular changeprovided by the beam steering optics between successively emittedpulses, is decreased. Temporarily increasing the laser pulse rate whilescanning the identified region(s) thus provides enhanced angularresolution, with respect to the LIDAR device, in those identifiedregion(s).

Another 3-D point cloud is generated from the LIDAR-sampled data (510).The point cloud generated in block 510 generally has greater angularresolution in the identified region(s), where the pulse rate wasincreased, than in other regions of the scanning zone such that theresulting 3-D point cloud has a non-uniform angular resolution (e.g., anon-uniform angular separation between adjacent points), with respect tothe position of the LIDAR device. The non-uniform 3-D point cloudgenerated in block 510 can then be further analyzed by the hardwaremodules and/or software components, such as the obstacle avoidancesystems 144, computer vision systems 140, sensor fusion algorithm 138,object detection systems, etc. to inform autonomous decision-making bythe vehicle 100 based on the indications of the surrounding environmentprovided by the non-uniform 3-D point cloud.

FIG. 5B is a flowchart 520 of a process for driving a LIDAR device at apulse rate temporarily exceeding a maximum sustained thermally stablepulse rate. In example embodiments, a scanning LIDAR (e.g., the LIDARdevice 302 described in connection with FIGS. 3A-3C) can have a maximumsustained pulse rate set by thermal characteristics of the

LIDAR device lasing media, associated optics, and/or heat sinks, etc. aswell as ambient conditions. For example, continuously operating theLIDAR device with a particular pulse rate results in a steady-statetemperature, where the rate of heat energy generation approximatelyequals the rate of heat dissipation. In general, the pulse intensity(e.g., laser brightness) and/or duty cycle (e.g., length of pulseduration as compared to interval between successive pulses) can alsoinfluence the steady state temperature, however, the intensity is setaccording to the desired distance sensitivity of the LIDAR device, andthe duration is set to be as short as practicable so as to maximize thetemporal sensitivity of the time delay measurement. Thus, the pulse rateof the LIDAR device in continuation operation is desirably selected toavoid undesirable thermally-driven effects in the output signal, such asintensity variations in the laser output, spontaneous optical noise,etc. and/or excessive degradation in the operating lifetime of thedevice, such as due to sustained operating temperatures above athreshold level, etc.

Some embodiments of the present disclosure allow for temporarilyincreasing the pulse rate from a default rate to exceed the maximumsustained pulse rate. To mitigate the thermal limits in the behavior ofthe device, exceeding the maximum sustained pulse rate is followed by aperiod of decreased pulse rate to allow the LIDAR device to thermallystabilize before returning to the default pulse rate. In some examples,the period of decreased pulse rate can optionally immediately followoperating in excess of the maximum sustained pulse rate. The defaultpulse rate can optionally be substantially similar to the maximumsustained pulse rate. Furthermore, some embodiments of the presentdisclosure allow for temporarily driving a LIDAR device at a rateexceeding a maximum sustained pulse rate when preceded by a period ofdecreased pulse rate to allow the LIDAR device to cool prior toexceeding the maximum sustained pulse rate. In some embodiments, thetotal duration of the temporary increase in the pulse rate is less thana characteristic thermal rise time of the LIDAR device itself.

The scanning LIDAR may be operated at a first pulse rate, such as adefault pulse rate, that is within its pulse-rate limit so as togenerate in real time a 3-D point cloud with a substantially uniformangular resolution based on the first pulse rate (502, 504).Concurrently with generation of the 3-D point map and in real time, aprocessor-based perception system identifies region(s) for enhancedresolution examination (506). The pulse rate of the LIDAR device istemporarily increased beyond its maximum sustained pulse rate while theLIDAR device scans the identified region(s) (522). The pulse rate isdecreased to allow the LIDAR device to thermally regulate (524). In someembodiments, the decreased pulse rate in block 524 is below the firstpulse rate (e.g., default pulse rate) and is maintained for a periodlong enough to allow the time-averaged pulse rate to be at or below themaximum sustained pulse rate. In some embodiments, the default pulserate itself is sufficiently below the maximum sustained pulse rate suchthat operating at the default pulse rate allows the LIDAR device tosufficiently thermally stabilizes following operation at the excesspulse rate.

A mixed-resolution 3-D point cloud is generated from the LIDAR-sampleddata points, with the high angular resolution regions sampled while theLIDAR device is driven in excess of its maximum sustained pulse rate,and default resolution, and/or low resolution regions sampled while theLIDAR device is driven at its default pulse rate, or low rate,respectively.

FIG. 6A symbolically illustrates a LIDAR device 302 scanning across anexample obstacle-filled environmental scene with constant time delays(e.g., the delay time t1) between successive measurement points wherethe time delays are exaggerated for illustrative purposes. The LIDARdevice 302 scans the laser beam 306 across the environmental scene viaits beam steering optics 304 while its laser light source pulses, suchthat successive pulses are emitted with an angular separation θ₁, whichis approximately given by the product of ω and t₁, where ω is theangular sweeping rate of the LIDAR device (e.g., the angular rotationrate of the beam steering optics 304), and t₁ is the time period betweensuccessive pulse emissions. As a result of the rotation of the beamsteering optics in the LIDAR device 302, temporally separated pulses(e.g., pulses emitted at times separated by the time t₁) are directed inrespective angular orientations separated by the amount of rotation ofthe beam steering optics during the interval t (e.g., the angle θ₁). Thetime scale of the interval between successive pulses, and thecorresponding resulting angular separations, is intentionallyexaggerated in FIG. 6A for illustrative purposes. A controller 630 isarranged to receive signals from the LIDAR device 302 and/or associatedoptical sensors to generate point cloud data 640 indicative of the 3-Dpositions of reflective features in the environmental scene surroundingthe LIDAR device 302.

FIG. 6B is a timing diagram of the transmitted and received pulses forthe exaggerated symbolic illustration of FIG. 6A. The timing diagramsymbolically illustrates the transmitted pulses (labeled on FIG. 6B as“Tx”), the received pulses (labeled on FIG. 6B as “Rx”), and the angularsweeping rate of the LIDAR device 302 (labeled on FIG. 6B as ω(t)).

An example operation of the LIDAR device 302 to achieve substantiallyuniform angular resolution is described in connection with FIGS. 6A and6B. At time Ta, a first pulse 610 a is emitted from the LIDAR device 302and directed along laser beam path 306 a via the beam steering optics.As shown in FIG. 6A, the beam path 306 a is reflected from near thefront passenger-side region of the car 310, and a first reflected signal620 a is detected at optical signals associated with the LIDAR device302 (e.g., via optical sensors included in the sensor system 202 mountedon the vehicle 200 in FIG. 2). The time delay between the emission ofpulse 610 a and reception of the reflected signal 620 a is indicated bytime delay ΔTa. The time delay ΔTa and the orientation of the LIDARdevice 302 at time Ta, i.e., the direction of laser beam 306 a, arecombined in the controller 630 to map the 3-D position of the reflectivepoint on the front passenger-side region of the car 310.

Next, at time Tb, which a second pulse 610 b is emitted from the LIDARdevice 302 and directed along laser beam path 306 b. Time Tb istemporally separated from time Ta by the interval time t1, and thedirection of the laser beam path 306 b is thus angularly separated fromthe direction of laser beam path 306 a by angular separation θ₁, due tothe change in orientation of the beam steering optics in the LIDARdevice during the interval t₁. Where the beam steering optics areundergoing a constant angular sweeping with an angular rate of changeω₀, the angle of separation θ₁ between the laser beams 306 a and 306 bis at least approximately given by the product of ω₀ and t₁. The laserpulse 310 b is reflected from near the rear passenger-side region of thecar 310, and a second reflected signal 620 b is detected with a relativetime delay ΔTb from the emission of the second pulse 610 b. Asillustrated in FIG. 6B, the LIDAR device 302 is generally situatedbehind the car 310, and so the reflective point near the rearpassenger-side region of the car 310 (responsible for the reflectedsignal 620 b) is closer to the LIDAR device 302 than the reflectivepoint near the front passenger-side region of the car 310 (responsiblefor the reflected signal 620 a). As a result, the relative time delayΔTb is shorter than the relative time delay ΔTa, corresponding to thedifference in roundtrip travel time at the speed of light between theLIDAR device 302, and the respective reflective points at the front andrear of the car.

Further, the sensors detecting the reflected signals can optionally besensitive to the intensity of the reflected signals. For example, theintensity of the reflected signal 620 b can be perceptibly greater thanthe intensity of the reflected signal 620 a, as shown symbolically inFIG. 6B. The controller 630 maps the 3-D position of the reflectivepoint near the rear passenger-side of the car 310 according to the timedelay value ΔTb and the orientation of the LIDAR device 310 at time Tb,i.e., the direction of laser beam 306 b. The intensity of the reflectedsignal can also indicate the reflectance of the reflective point, incombination with the distance to the point as indicated by the measuredtime delay. The reflectance of the reflective point can be employed bysoftware and/or hardware implemented modules in the controller 630 tocharacterize the reflective features in the environment. For example,traffic indicators such as lane markers, traffic signs, traffic signals,navigational signage, etc., can be indicated in part based on having arelatively high reflectance value, such as associated with a reflectivecoating applied to traffic and/or navigational signage. In someembodiments, identifying a relatively high reflectance feature canprovide a prompt to undertake a further scan of the high reflectancefeature with one or more sensors, such as those in the sensing system104. Thus, in one example, a reflected signal indicating a highreflectance feature can provide a prompt to image the high reflectancefeature with a camera to allow for identifying the high reflectancefeature. In some embodiments where the high reflectance feature is atraffic sign, the camera image can allow for reading the sign viacharacter recognition and/or pattern matching, etc. and then optionallyadjusting navigational instructions based on the sign (e.g., a signindicating a construction zone, pedestrian crosswalk, school zone, etc.can prompt the autonomous vehicle to reduce speed).

At time Tc, following the time Tb by the interval t1, a third pulse 610c is emitted from the LIDAR device 302. The third pulse 610 c isdirected along a laser beam path 306 c, which is approximately angularlyseparated from the beam path 306 b by the angle θ₁. The pulse 610 c isreflected from a point near the middle of the rear bumper region of thecar 310, and a resulting reflected signal 620 c is detected at the LIDARdevice 302 (or its associated optical sensors). The controller 630combines the relative time delay ΔTc between the emission of pulse 610 cand reception of reflected signal 620 c and the orientation of the LIDARdevice 302 at time Tc, i.e., the direction of beam path 306 c, to mapthe 3-D position of the reflective point.

At time Td, following time Tc by the interval t₁, a fourth pulse 610 dis emitted from the LIDAR device 302. The fourth pulse 610 d is directedalong a laser beam path 306 d, which is approximately angularlyseparated from the beam path 306 c by the angle θ₁. The beam path 306 dentirely avoids the car 310, and all other reflective environmentalfeatures within a maximum distance sensitivity of the LIDAR device 302.As discussed above, the maximum distance sensitivity of the LIDAR device302 is determined by the sensitivity of the associated optical sensorsfor detecting reflected signals. The maximum relative time delay ΔTmaxcorresponds to the maximum distance sensitivity of the LIDAR device(i.e., the time for light signals to make a round trip of the maximumdistance). Thus, when the optical sensor associated with the LIDARdevice 302 does not receive a reflected signal in the period ΔTmaxfollowing time Td, the controller 630 determines that no reflectivefeatures are present in the surrounding environment along the laser beampath 306 d.

The reflective points on the car 310 corresponding to the reflectedsignals 610 a-c form a subset of points included in a 3-D point cloudmap of the environment surrounding the LIDAR device 302. In addition,the direction of the laser beam 310 d is noted in the 3-D point cloudmap 640 as being absent of reflective features along the line of sightwithin the maximum distance sensitivity of the LIDAR device 302, becauseno reflected signal was received after the duration ΔTmax following theemission of pulse 610 d at time Td. The points corresponding to laserbeam directions 306 a-d are combined with points spaced throughout thescanning zone (e.g., the region scanned by the LIDAR device 302), tocreate a complete 3-D point cloud map, and the results are output asfixed resolution point cloud data 640 for further analysis by objectdetection systems, pattern recognition systems, computer vision systems,etc. As described above, the points in the generated point cloud areseparated by a substantially constant angular separation θ₁ with respectto the LIDAR device 302 due to the substantially constant angularsweeping rate ω₀ and the regular pulse rate of the LIDAR device 302 withinterval timing t₁.

FIG. 6C symbolically illustrates the LIDAR device 302 scanning acrossthe example obstacle-filled environmental scene shown with adaptivelyadjusted time delays between successive measurement points to provideenhanced angular resolution in the vicinity of the environmentalobstacle where the time delays are exaggerated for illustrativepurposes. The LIDAR device 302 is operated to scan through thesurrounding scene with angular rate of change ω₀, as in FIG. 6A.However, the pulse rate of the LIDAR device 302 is dynamically adjustedto vary from a pulse rate with intervals of approximately t₁ tointervals of approximately t₂, where t₂ is less than t₁. While operatedat the high pulse rate (with intervals between successive pulseemissions of t₂, rather than t₁), the LIDAR device 302 provides angularseparations between successive points of θ₂, given approximately by theproduct of ω₀ and t₂. Thus, the angular resolution of the resultingpoint cloud (e.g., as determined by the angular separation of adjacentpoints in the generated point cloud) is modified according to theinterval spacing between subsequent pulses.

As shown in FIG. 6C, the pulse rate of the LIDAR device 302 is set to gohigh while the LIDAR device is directed toward the car 310. Thedetermination to sample at high frequency while directed to the car 310can optionally be determined, at least in part, based on analysis of oneor more previous point clouds sampled with fixed angular resolution,such as described in connection with FIGS. 6A and 6B above. For example,the region of the environmental scene responsible for generating thereflected signals 620 a-c can be selected for high resolutionexamination in a subsequent scan. Detailed discussions of techniques foridentifying regions of an environmental scene for examination atenhanced (“increased”) angular resolution are presented below inconnection with FIGS. 7A-7G.

FIG. 6D is a timing diagram of the transmitted and received pulses forthe exaggerated symbolic illustration of FIG. 6C. Similar to the timingdiagram in FIG. 6B, the timing diagram symbolically illustrates thetransmitted pulses (labeled on FIG. 6D as “Tx”), the received pulses(labeled on FIG. 6D as “Rx”), and the angular sweeping rate of the LIDARdevice 302 (labeled on FIG. 6D as ω(t)).

In an example operation of the LIDAR device 302 according to the timingdiagram of FIG. 6D, a first pulse 610 e is emitted from the LIDAR device302 at time Te, and directed along laser beam 306 e. No reflected signalis detected during the period ΔTmax following the time Te. An indicationof an absence of reflective features within the maximum distance ofsensitivity is noted by the controller 630 in connection with theorientation of the LIDAR device 302 at time Te (i.e., the direction oflaser beam 306 e).

At time Tf, following Te by the duration t₁, a second pulse 610 f isemitted from the LIDAR device 302. The second pulse 610 f is directedalong a laser beam 306 f, which is oriented with an angular separationθ₁ from the laser beam 306 e. The second pulse 610 f reaches the frontpassenger-side region of the car 310 resulting in a reflected signal 620f. The reflected signal 620 f is detected by the LIDAR device 302 and/orits associated optical sensors with a relative time delay of ΔTffollowing the emission at time Tf.

The pulse 610 f marks the beginning of a temporary high pulse rateoperation of the LIDAR device 302. The third pulse 610 g is emitted attime Tg, which follows time Tf by an interval t₂, which interval is lessthan the interval t₁ between successive pulses while the LIDAR device302 operates at its default pulse rate. The third pulse 610 g isdirected along laser beam path 306 g, which has an orientation thatdiffers from the direction of beam path 306 f by the angle θ₂. Theangular separation θ₂ between the two beam paths 306 f and 306 g is due,at least in part, to the angular rotation of beam steering optics in theLIDAR device 302 (e.g., the rotating angled mirror 304 of FIG. 3A)during the interval t₂. Thus, while the angular rate of change oforientation of the LIDAR device (such as via rotation of associated beamsteering optics) is approximately constant at ω₀, the angle θ₂ is givenapproximately by the product of co_(o) and t₂. The third pulse 610 g isreflected from a point near the passenger-side door of the car 310, anda corresponding reflected signal 620 g is detected by the LIDAR device302 with relative time delay ΔTg.

The high rate scanning continues through the region including thevehicle 310, beginning with pulse 610 h emitted along laser beam path306 h, such that the angular resolution of the generated point cloud forpoints on and/or near the vehicle 310 is generally greater than regionsscanned with the default pulse rate. That is, angular separation betweenpoints on or near the car 310 is approximately θ₂, while the angularseparation of points in the point map located elsewhere is approximatelyθ₁, and θ₂ is less than θ₁. In some embodiments, the high resolution(“high pulse rate”) scanning can continue until a pulse (or series ofrepeated pulses) is emitted that does not result in a reflected responsesignal, which can be interpreted as scanning past a reflective featureof interest.

The controller 630 receives information from the LIDAR device 302 togenerate a 3-D point cloud map indicating the locations of reflectivefeatures and/or the absence of reflective features within a maximumdistance of sensitivity. The angular resolution of the distribution ofpoints in the generated point cloud is non-uniform, with respect to theLIDAR device 302. In particular, some regions of the environment aresampled at a high pulse rate, and corresponding high angular resolution(e.g., the region including the car 310 sampled with pulses 306 f-h,etc.), whereas other regions are sampled at the default pulse rate. As aresult, some embodiments described herein describe the generated pointcloud map as a mixed resolution point cloud. Data indicative of thepositions of points in the point cloud map are output as mixedresolution point cloud data 642.

In some embodiments, the operation described in connection with FIGS. 6Aand 6B to generate the fixed resolution point cloud data 640 cancorrespond to the blocks 502 and 504 of the flowchart 500 in FIG. 5A ofthe process for adaptively adjusting the pulse rate timing to generate apoint cloud with non-uniform angular resolution. Similarly, in someembodiments, the operation described in connection with FIGS. 6C and 6Dto generate the mixed resolution point cloud data 642 can correspond tothe blocks 508 and 510 of the flowchart 500 in FIG. 5A. The processesfor identifying regions of a LIDAR-indicated scene for enhancedresolution examination described below in connection with the flowchartsshown in FIGS. 7A-7G provide example processes for implementing block506 of the flowchart 500 in FIG. 5A.

According to some embodiments of the present disclosure, a region of aLIDAR-indicated point cloud can be identified for enhanced angularresolution analysis based on a combination of factors. For example,enhanced angular resolution analysis can be initiated in response toidentifying factors in one or more of the point cloud distance mapprovided by the LIDAR device, the intensity map of reflected light(e.g., from the LIDAR-received reflected light signals), an estimate ofthe location of the autonomous vehicle and/or pre-mapped objects ofinterest, an output from additional sensors such as the camera 210, etc.The estimated location of the autonomous vehicle and/or objects ofinterest can be based on comparing the dynamically generated 3-D pointmap with a baseline map of the scanning zone showing fixed objects ofinterests (e.g., lane markers, intersection locations, etc.).

FIG. 7A is a flowchart 710 of a process for identifying regions of aLIDAR-indicated point cloud to examine with enhanced angular resolutionby detecting edge(s). One or more point clouds from previous scans of ascanning zone are analyzed (712). In some embodiments, the analysis canbe carried out via hardware and/or software components, such as thecomputer system 112 and associated processor 114, the computer visionsystem 140, and/or sensor fusion algorithm module 138 referred to inFIG. 1 in connection with the vehicle 100. An object detection moduleand/or software module identifies a reflective feature in the scanningzone indicated by the point cloud data (714). Once identified, thelocations of the edge or edges of the feature are set as the regions toscan with enhanced angular resolution during the next scan (716).Locating edge(s) (716) can include estimating the dimensional extent ofidentified objects as indicated point cloud groupings in one or moreprevious scans of the environmental scene, which previous scans can beentirely default angular resolution scans and/or mixed resolution scans.

In some examples, edge locations can be estimated (716) according todetected discontinuities in distance measurements for adjacent or nearbypoints in the generated point cloud. That is, in some examples a cluster(“group”) of points in a point cloud approximately define a surfaceassociated with an environmental physical object or fixed feature.Hardware and/or software implemented object detection and/or recognitionmodules can associate the cluster of points with environmentalobjects/features. In such an example, the surface defined by the pointcluster is surrounded, at least partially, by points that are not partof a continuous surface including the point cluster. Thus, the edges ofviewable portions of perceived objects can be indicated by a suddenincrease in line-of-sight distance where a relatively distant backgroundis visible along a line of sight from the LIDAR device 302 immediatelyadjacent the perceived object. Additionally or alternatively, the edgesof viewable portions of some objects can be indicated by a suddendecrease in line-of-sight distance where a closer object interferes withviewing the entire object.

In some examples, edge locations are estimated (716) according topredicted object dimensions, as indicated by standard libraries of, forexample, car dimensions, pedestrian heights, etc. objects are locateedges of detected objects or features indicated by the generated pointcloud data (714). The location of the edges in the environmental scenecan then be based on predicted locations of edge(s) of perceivedobjects, as informed by standard dimension libraries, even where theperception and/or identification of a particular object is based on onlya portion of the object.

FIG. 7B is a flowchart of another process for identifying regions of aLIDAR-indicated point cloud to examine with enhanced angular resolutionby detecting moving objects. One or more previous scans of a scanningzone are analyzed (712). As discussed above in connection with thehardware and/or software implemented object detection and/or objecttracking functions, perceived objects in each generated point cloud areassociated with a boundary approximately defining a shape of theperceived objects. Objects appearing in more than one frame of a timeseries of generated point clouds are associated with one another basedon similar point cloud shapes and/or absolute relative positions fromone frame to the next. Thus, the same object can be identified andposition mapped at a series of time points so as to determine a motionprofile of the object. Thus, an object in motion can be identified fromthe point cloud data (724). Moreover, moving objects can bedistinguished from non-moving objects. The locations of such movingobjects during the next scan can be predicted, and the predictedlocation can be set as the region for enhanced resolution study (726).

In some instances, the region set for enhanced resolution study duringblock 726 includes a characteristic uncertainty in the future positionof the moving object. The region for enhanced resolution study cantherefore be larger than the predicted size of the moving object asdetermined by the point cloud data. For example, the region for enhancedstudy can include the predicted location of the moving object during thenext scan, and can be enlarged, relative to the size of the movingobject, according to the uncertainty of the predicted location. Theuncertainty can be formulaic, such as based on the expected size of themoving object, or a standard angular separation surrounding the positionof the moving object. Additionally or alternatively, the uncertainty canbe based on empirical factors and/or measurement uncertainty in theposition and/or motion profile of the moving object, for example.

For example, the car 310 described in connection with FIG. 6C can be inmotion with respect to the LIDAR device 302. That is, the relativeposition of the car 310 with respect to the LIDAR device 302 can varybetween a first scan (e.g., the fixed resolution scan described inconnection with FIGS. 6A and 6B) and a second, high angular resolutionscan. Thus, identifying the region of the environment to scan thatincludes the car 310 during the second scan includes predicting a futurelocation of the car 310 based on a motion profile (e.g., speed anddirection) for the car 310.

Some embodiments also allow for detecting and characterizing movingobjects based on real time analysis of a single three-dimensional pointcloud map. For example, due to time delays between measuring points indifferent regions of the scanning zone, there may be some detectablemotion of perceived objects within a single frame. In some instances,the scanning zone is analogous to an arrangement of points distributedin azimuth and elevation, to form a grid-like structure, with each rowhaving a common altitude orientation, and each column having a commonazimuth orientation. Where a LIDAR device operates by rotating at afixed altitude orientation to sample azimuthal variations before movingto the next altitude value, the relative time delay between scans atdistinct altitude values can be comparable to the period for a completescan. At sufficiently slow refresh rates (and/or for sufficientlyrapidly moving objects) the “vertical” edges of objects (i.e., edgeswith approximately instantaneously constant azimuth) appear to beslanted, as successive altitude scans detect the moving object atslightly different locations. Thus, generation of a real-time 3-D pointmap can be used to detect and measure relative motion between thescanning LIDAR and a transient object in the scanning zone (724). Bymeasuring relative motion, hardware and/or software elements of theperception system can also predict a trajectory of the moving object.The perception system also predicts the future location of the movingobject, and thereby identifies the regions of the scanning zone for highresolution mapping as a function of time (726).

Some embodiments of the present disclosure include selectively scanningmoving objects with high angular resolution so as to better characterizethe position and/or motion profile of such objects. Establishingaccurate positions of moving objects allows for precisely characterizingthe motion profile of the object over time such that the location of theobject can be predicted with greater accuracy than possible withoutenhanced angular resolution analysis. Thus, some embodiments of thepresent disclosure operate to selectively allocate scanning resources toresolving the positions and/or motion profiles of moving objects in thescanning zone so as to enable greater precision in future predictedlocations of such objects, and thereby enable greater precision inobject avoidance.

Moreover, the techniques for identifying locations to scan with highangular resolution described in connection with FIGS. 7A and 7B, can becombined together. For example, the region to scan with high angularresolution can be the predicted location of edge(s) of a moving object.

FIG. 7C is a flowchart of another process for identifying regions of aLIDAR-indicated point cloud to examine with enhanced angular resolutionby comparison with a baseline point map. A baseline point map can be,for example, a three-dimensional map of fixed structures along a roadwaythat is generated in advance. Such a baseline map can indicate, forexample, the approximate three-dimensional locations of curbs, trees,buildings, road signs, permanent barricades, and/or other substantiallypermanent objects and environmental features defining the surroundingsof a roadway or other vehicular path of travel. In some examples, thebaseline 3-D map is generated by a LIDAR device actively monitoring ascene while traversing the scene substantially in the absence oftransient objects. Additionally or alternatively, the baseline 3-D mapcan be generated by combining multiple maps to distinguish fixedfeatures from transient objects. Additionally or alternatively, thebaseline 3-D point map can be generated via recognition/categorizationsoftware and/or hardware implemented modules that analyze one or moremaps to selectively eliminate reflective features identified ascorresponding to transient objects, such as reflective featuresidentified as cars, pedestrians, etc. The baseline 3-D map can be loadedin digital form and stored locally in the data storage 114 of thecomputer system 112 and/or can be dynamically downloaded from anexternal server via the communication system 146.

One or more LIDAR-indicated point cloud maps of the scanning zone areanalyzed via the hardware and/or software implemented data analysismodules to identify regions for high resolution study (712). Based onthe approximate position of the scanning zone, which can be based on thesensing system 104, such as the GPS 122, a region of the baseline map isidentified that corresponds to the scanning zone (e.g., a region of thebaseline map at substantially the same geographic location as thescanning zone). The dynamically generated 3-D point cloud maps arecompared with the corresponding region of the baseline map (734). Thesoftware and/or hardware implemented perception systems identifydifferences between the dynamically generated point cloud map(s) (736).Reflective features indicated in the dynamically generated point couldmap(s) that are not present in the baseline map are set for enhancedresolution scanning. That is, the region identified for enhancedresolution scanning in block 506 of FIG. 5A is set to include thelocations of reflective features indicated by the real time point cloudscans, but not indicated by the baseline 3-D map. In some embodiments,reflective features indicated in the dynamically generated point cloudmaps that are determined to also be present in the baseline map areidentified as fixed features, whereas reflective features not present inthe baseline map are identified as transient objects. Scanning suchdynamically indicated, presumptively transient objects with enhancedangular resolution allows for greater accuracy in position and/or motionprofile determinations and thereby allows for greater precision inobstacle avoidance.

In some embodiments, the baseline 3-D map is analyzed to identifyinitial regions for low-resolution scanning and/or high-resolutionscanning. For example, regions of the baseline 3-D map that indicate thepresence of an intersection, a traffic control signal, a railroadcrossing, etc. can be prioritized for high resolution scanning at theexpense of regions of the baseline 3-D map that indicate blue sky, abody of water, or another region associated with a low probability ofcausing an obstacle. Upon analyzing additional scans, the initialresolution can be dynamically varied according to differences with thebaseline 3-D map, as described in connection with FIG. 7C, and/oraccording to any of the processes described in FIGS. 7A-7G.

FIG. 7D is a flowchart 740 of another process for identifying regions ofa LIDAR-indicated point cloud to examine with enhanced angularresolution by detecting features beyond a threshold distance. One ormore LIDAR-indicated point cloud maps of the scanning zone are analyzedvia the hardware and/or software implemented data analysis modules toidentify regions for high resolution study (712). Perceived objects(e.g., clusters of points) located at a distance beyond a thresholddistance are identified for enhanced resolution study (744). Theidentified region is then scanned with an enhanced angular resolution(e.g., by using an increased pulse rate) and an output mixed resolutionpoint cloud is analyzed to better characterize the distant object.

The threshold distance can be set according to a desired spatialresolution to allow object identification/categorization with desiredreliability. The spacing between adjacent points in the point cloud isgiven, at least approximately, by the arc length distance mapped by theangular change in the LIDAR orientation at the line of sight distance ofthe mapped features. Because arc length scales with radius (line ofsight distance), the spatial resolution achieved by the LIDAR device isinversely proportionate to the line of sight distance to reflectivefeatures.

Thus, distant objects can have relatively low spatial resolution whichprevents accurate object identification/categorization. For example, inthe case of an approaching car that first appears as a very small objecton the horizon, scanning the distant car with enhanced angularresolution, allows the car to be identified and its position and motionto be characterized sooner than otherwise possible, which enables objectavoidance and similar functions to be undertaken sooner.

In some instances, distant objects can optionally be identified forenhanced resolution study only when such distant objects are locatedwithin a zone including an anticipated direction of travel of thevehicle (e.g., within a zone defined by a cone with an apex at thevehicle and a base oriented toward a heading of the vehicle). Forexample, distant objects located directly to either side of a movingvehicle are not set for enhanced resolution study whereas distantobjects located directly in front of the vehicle are set for enhancedresolution study. In some instances, distant objects are identified forenhanced resolution study only when such distant objects are located ina region of the environmental scene warranting further study, such as onanother roadway. Regions of the scene warranting further study, such asroadways, etc., can be indicated by the sensor system 104, computervision system 140, etc., based on lane lines, curbs, etc.

FIG. 7E is a flowchart 750 of another process for identifying regions ofa LIDAR-indicated point cloud to examine with enhanced angularresolution by detecting a discontinuity during a scan corresponding toan object edge. A dynamically generated point cloud is analyzed byhardware and/or software implemented data analysis modules (752). Inparticular, the technique demonstrated by block 752 analyzes data from apartial point cloud that is currently being sampled. Data from thecurrent, partially complete point cloud is analyzed to identify localdiscontinuities in the distribution of point locations (754). Forexample, similar to the discussion of the technique for edge locationdescribed in connection with FIG. 5A, a cluster of points can beconsidered as being approximately located along a 3-D surface thatdefines an outer boundary of a reflective object with line of sight tothe scanning LIDAR. The surface defining a single reflectiveobject/feature generally bends and curves according to the shape of theobject defined, and so is not necessarily planer. However, at the edgesof such objects, the line of sight distances measured by the LIDARdevice suddenly change from values roughly in the bounding surface tomuch further or closer distances, without an indication of any gradualbend or transition.

Such local discontinuities can be identified as an edge of an object. Inresponse, the LIDAR device can be set to perform high angular resolutionsampling so as to better define the edge of any such objects (756). Insome embodiments, the high resolution study (756) can be initiatedwithout involvement the object detection software and/or hardwaremodules to analyze a complete scan, identify objects in the scan, andthen locate edges of those objects. The process illustrated by theflowchart 700 in FIG. 7E allows for rapid initiation of high resolutionscanning to study indicated edges in a currently sampled scan, based onpartially taken results from the same, current scan.

FIG. 7F is a flowchart 760 of another process for identifying regions ofa LIDAR-indicated point cloud to examine with enhanced angularresolution by identifying features arranged with high spatial ortemporal frequency. Hardware and/or software implemented data analysismodules analyze LIDAR-indicated point cloud data from present and/orprevious scans of a scanning zone (762). Regions of the scanning zonewith relatively high spatial and/or temporal frequencies of reflectivefeatures are located (764). Regions of the scanning zone are selectedfor enhanced resolution scanning based on the spatial and/or temporalfrequency of the reflective features in those regions (766). Forexample, the regions with highest spatial and/or temporal frequency canbe ranked and the regions with the highest rankings can be selected forenhanced resolution examination up to the available capacity of theLIDAR system for undergoing enhanced resolution study. In otherexamples, the regions selected for enhanced resolution study canpreferentially select high temporal frequency regions (e.g., regionsincluding moving objects) and select high spatial frequency regions(e.g., regions of the point cloud map including large distancevariations, which can indicate object edges) subject to availability ofresources, or vice versa. Furthermore, some embodiments includecombinations of these approaches where an initially selected region isbased on the spatial frequency of the selected region, but is onlyscanned with high angular resolution again, based on an observed changein the spatial frequency of the reflective features in the region.

In some aspects, the selection of high resolution scanning regions basedon relative spatial and/or temporal frequency of the point cloud map canbe considered a combination of techniques described in connection withthe flowcharts 710, 720 of FIGS. 7A and 7B to select regions includingedges or objects in motion. High spatial frequency regions of thescanning zone include regions with relatively large differences indistance measures between angularly nearby points, such as due to thediscontinuity effects near the edges of perceived objects. High temporalfrequency regions of the scanning zone include regions with points inthe point cloud that vary between successively generated point cloud mapframes, such as due to an object in motion that changes position betweensuccessive frames of the point cloud map.

FIG. 7G is a flowchart 770 of another process for identifying regions ofa LIDAR-indicated point cloud to examine with enhanced angularresolution by detecting features that lack sufficient detail foraccurate categorization or identification. Hardware and/or softwareimplemented data analysis modules analyze LIDAR-indicated point clouddata from present and/or previous scans of a scanning zone (772).Regions of the scanning zone including reflective features that cannotbe identified or categorized with sufficient reliability (e.g., asindicated by an estimated probability of accurate identification) arelocated (774). Regions of the scanning zone are selected for enhancedresolution scanning based on the indication of unidentified reflectivefeatures in those regions (776). By scanning the unidentified reflectivefeatures with enhanced resolution, additional spatial resolution isprovided so as to provide additional information with which to identifythe reflective feature, and thereby provide a more accurateinterpretation of the surrounding environment.

For example, reflective features can be identified by correlatingclusters and/or patterns of points in the point cloud with shapes and/orsurfaces defining the boundaries of the reflective feature, andcross-referencing the geometry of the shape against a library ofgeometries associated with possible objects, such as pedestrians,bicycles, cars, motorcycles, construction equipment, bridges, firehydrants, mail boxes, road signs, trees, animals, combinations of these,etc. Where reflective features are not indicated with sufficient spatialresolution, such as for distant objects, it may be too difficult toreliably distinguish, based on LIDAR-indicated point clouds, whether areflective object is a traffic cone or a fire hydrant. Thus, increasingLIDAR angular resolution in regions where an object is perceived to belocated, but cannot be accurately identified allows for selectivelyproviding additional information to identify the object.

The technique of FIG. 7G described in connection with flowchart 770employs the output of the hardware and/or software implemented objectdetection system as feedback for the operation of the LIDAR system. TheLIDAR system thereby selectively allocates its enhanced resolutionscanning resources to regions of the scanning zone with insufficientspatial resolution to allow for reliable object detection.

In some embodiments, reliable identification of perceived objects allowsfor greater obstacle avoidance. For example, identified objects can beassociated with typical motion and/or speed profiles that allow forenhanced prediction of future locations.

As noted above, the techniques for identifying regions of a scanningzone for enhanced resolution scanning described in connection with theflowcharts 710, 720, 730, 740, 750, 760, 770 in FIGS. 7A-7G can eachindividually or as a combination of one or more be implemented as block506 of the flowchart 500 described in connection with FIG. 5A.

FIG. 8 is a flowchart 802 of a process for adaptively adjusting anangular resolution of a LIDAR device by adjusting a slew rate of theLIDAR device. Similar to the process described in connection with theflowchart 500 in FIG. 5A, a LIDAR device is scanned through a scanningzone (502), a 3-D point cloud data is generated from the LIDAR output(504), and regions of the point cloud are identified for enhancedresolution study (506). To provide high angular resolution scanning,however, the slew rate of the beam steering optics of the LIDAR deviceis selectively slowed while scanning the selected regions (808). A mixedresolution 3-D point cloud is generated that includes relatively highangular resolution in the identified regions due to the small rate ofthe change of the beam steering optics while scanning those regions(810).

For example, for the LIDAR device 302 including an angled rotatingmirror 304, the rotation rate of the angled rotating mirror 304 istemporarily slowed such that the relative angular change betweensuccessive pulses of the laser is decreased according to the rate ofrotation. Thus, the slew rate of the LIDAR device referred to herein isthe angular rate of change of the orientation of the laser pulsesemitted from the LIDAR device. While the changing orientation isillustrated and described herein as achieved via an angled rotatingmirror, the changing orientation is generally provided by beam steeringoptics that include a suitable combination of lenses, mirrors,apertures, etc. to effect the changing orientation of the pulsing laserdescribed herein.

FIG. 9A symbolically illustrates a LIDAR device 302 scanning across anexample obstacle-filled environmental scene with adaptively adjustedslew rate of the LIDAR device to provide enhanced angular resolution inthe vicinity of the environmental obstacle where the time-scale isexaggerated for illustrative purposes. The LIDAR device 302 scans thelaser beam in the direction indicated by directional reference arrow901. While scanning, the laser light source in the LIDAR system 901emits pulses separated by the duration t₁. Thus, the angular separationbetween successively emitted pulses is given approximately by theproduct of the angular rate of change of the beam steering optics andthe interval between successive pulses (e.g., approximately the productof ω(t) and t₁). Thus, reducing the slew rate of the LIDAR device 302reduces the angular separation between successively emitted pulses. Thetime scale of the interval between successive pulses, and thecorresponding resulting angular separations, is intentionallyexaggerated in FIG. 9A for illustrative purposes to show changes inangular separation. The controller 630 is arranged to receive signalsfrom the LIDAR device 302 and/or associated optical sensors to generatethe mixed resolution point cloud data 642.

FIG. 9B is a timing diagram of the transmitted and received pulses forthe exaggerated symbolic illustration of FIG. 9A. Similar to the timingdiagrams provided above at FIGS. 6B and 6D, the timing diagram in FIG.9B includes symbolic indications of transmitted pulses emitted from theLIDAR device 302 (labeled “Tx”); received reflected signals detected viaassociated optical sensors in the sensor system 104 (labeled “Rx”); andangular rate of rotation of the beam steering optics, or LIDAR slew rate(labeled “ω(t)”).

In an example operation of the LIDAR device 302 according to the timingdiagram of FIG. 9B, a first pulse 912 is emitted from the LIDAR device302 at time Ti, and directed along laser beam 902. No reflected signalis detected during the period ΔTmax following time Ti. An indication ofan absence of reflective features within the maximum distance ofsensitivity is noted by the controller 630 in connection with theorientation of the LIDAR device 302 at time Te (i.e., the direction oflaser beam 912).

At time Tj, following Ti by the duration t₁, a second pulse 914 isemitted from the LIDAR device 302. The second pulse 914 reaches thefront passenger-side region of the car 310 resulting in a reflectedsignal 924. The reflected signal 924 is detected by the LIDAR device 302and/or its associated optical sensors with a relative time delay of ΔTjfollowing the emission at time Tj. The second pulse 914 is directedalong a laser beam path 904, which is oriented with an angularseparation θ₁ from the beam path 902. The angular separation θ₁ isapproximately given by the product of ω₀ and t₁. However, slew rate w(t)of the beam steering optics associated with the LIDAR device 302 isdecreased from ω₀ to ω_(low) at time Tj to provide enhanced angularresolution scanning beginning at time Tj while the LIDAR device 302begins scanning across the region including the car 310. As a result,the angle θ₁ is more properly defined according to an integral of ω(t)from Ti to Tj, but the estimated value of θ₁ is a good approximationwhere the slew rate adjustment is made nearly instantly at the time Tj.

The pulse 914 at time Tj marks the beginning of a temporary enhancedresolution scanning operation of the LIDAR device 302. The third pulse916 is emitted at time Tk, which follows time Tj by an interval t₁. Thethird pulse 916 is directed along laser beam path 906, which has anorientation that differs from the direction of beam path 904 by theangle θ₃. The angular separation θ₃ between the two beam paths 904 and906 is due, at least in part, to the angular rotation of beam steeringoptics in the LIDAR device 302 with slew rate ω_(low) (e.g., therotating angled mirror 304 of FIG. 3A) during the interval t₁ betweentime Tj and time Tk. Thus, while the angular rate of change oforientation of the LIDAR device (such as via rotation of associated beamsteering optics) is approximately constant at ω_(low), the angle θ₃ isgiven approximately by the product of ω_(low) and t₁. The third pulse916 is reflected from a point near the passenger-side door of the car310, and a corresponding reflected signal 926 is detected by the LIDARdevice 302 with relative time delay ΔTk.

A fourth pulse 918 is emitted and time Tl following time Tk by theinterval The LIDAR device continues at the decreased slew rate ω_(low)during the interval between Tl and Tk, such that the pulse 918 isdirected along laser beam path 908, which differs in orientation fromthe pulse 906, at least approximately, by the angle θ₃. By scanning atthe decreased slew rate ω_(low), the LIDAR device 302 provides enhancedangular resolution scanning even without adjusting the pulse rate of theLIDAR device 302. The enhanced resolution scanning can be continueduntil the LIDAR device 302 scans substantially the entire car 310 beforereturning to the default slew rate ω₀.

The controller 630 receives indications of orientations of the LIDARdevice 302 at each time point and associates each orientation with thetime delay observed for reflected signals, or the absence of suchsignal. The information is combined to generate 3-D position mapping foreach point in a point cloud surrounding the LIDAR device 302 and theresulting mixed resolution 3-D point cloud data 642 is output forfurther analysis.

Generally, while described separately, the techniques described hereinto provide enhanced angular resolution LIDAR scans by adjusting pulserate (FIG. 5A) or by adjusting slew rate (FIG. 8) can be combinedtogether to provide a desired angular resolution. For example, enhancedangular resolution can be achieved by simultaneously increasing pulserate and decreasing slew rate. Moreover, the two techniques can beemployed systematically and in complementary fashion to account forrespective limitations. For example, whereas decreasing slew rate cangenerate mechanical stresses when rapid adjustments are made, increasingpulse rate can generate thermal stresses when carried out for too long,as discussed in connection with FIG. 5B above. Thus, some embodiments ofthe present disclosure provide for initiating high resolution scanningby initially increasing laser pulse rate. The slew rate can be graduallyslowed, rather than rapidly slowed, and the laser pulse rate can begradually decreased in correspondence with the decrease in the slew ratesuch that the angular resolution achieved remains substantially similar.To allow for relatively long duration high resolution scanning, the slewrate can be slowed to an amount sufficient to set the laser pulse rateto its default value, and thereby allow the laser system to thermallyregulate. At the conclusion of the enhanced resolution scan, the processcan be reversed to gradually increase pulse rate while increasing pulserate in corresponding fashion to substantially maintain the enhancedangular resolution. The slew rate returns to its default value at theconclusion of the enhanced resolution scan, at which point the laserpulse rate is also decreased to its default. The above is merely oneexample of a non-limiting combination of the two techniques forgenerating enhanced resolution LIDAR scans that is provided tofacilitate to explanation and understanding.

FIG. 10 is a flowchart 1000 of a process for generating a point cloudwith variable angular resolution by use of a second LIDAR device withenhanced angular resolution.

FIG. 11 is a block diagram of an obstacle detection system with a firstLIDAR device 302 providing default resolution scans of a scanning zoneand a second LIDAR device 1104 providing enhanced angular resolutionscans of identified regions. The process illustrated by the flowchart1000 is described with reference to the example two LIDAR systems shownin FIG. 11.

The first LIDAR device 302 is scanned through a scanning zone at adefault resolution (1002). The first LIDAR device 302 includes a laserlight source emitting pulsed laser beams (e.g. the example laser beampath 306) directed by beam steering optics, which can include a rotatingangled mirror 304. The rotating angled mirror 304 is operated at adefault slew rate and the laser light source is operated at a defaultpulse rate so as to provide complete scans of the scanning zone at anestablished refresh rate. In some embodiments, the angular resolution ofthe first LIDAR device 302 can be fixed at a suitable default valuesufficient to continuously scan the scanning zone at the refresh rate.

Data from the scan by the first LIDAR device 302 and data indicatingreturning reflected signals is analyzed to generate a three-dimensionalpoint cloud (1004). The 3-D point cloud can be generated by thecontroller 630, which can include hardware and/or software implementedmodules for analyzing the data to extract time delays from returningsignals, and combine the time delays with orientations of the LIDARdevice 302 to generate the point cloud. The same and/or additionalhardware and/or software implemented modules analyze one or more of thegenerated point clouds according to the techniques discussed inconnection with FIGS. 7A-7G or combinations thereof to identify regionsof the scanning zone for examination at enhanced resolution (1006).

Indications of the locations of the identified regions in the scanningzone are communicated to the second LIDAR device 1102, which is thenoperated to selectively scan the identified regions (1008). In someembodiments, the second LIDAR device 1102 is a dedicated high resolutionLIDAR device that is configured to operate at enhanced angularresolution. The second LIDAR device 1102 includes beam steering optics1104, which can be implemented as a rotating angled mirror to direct theoutput from a laser light source to a region of the scanning zone to bescanned (e.g., along the example laser beam path 1106). To achieveenhanced resolution with the second LIDAR device 1102, the rotatingangled mirror 1104 can provide a relatively lesser slew rate than theslew rate of the first LIDAR device. Additionally or alternatively, thesecond LIDAR device 1102 can operate with a relatively greater laserpulse rate than the first LIDAR device 302.

Information from both LIDAR devices 302, 1102 is received at thecontroller 630 and combined with optical sensor data indicatingreception of reflected signals, or lack thereof, to generate the mixedresolution point cloud data 642 (1010). In some embodiments, to avoidconfusion in the optical sensors detecting reflected signals betweenpulses emitted from the two LIDAR devices 302, 1102, the two are notoperated simultaneously. For example, the second LIDAR device 1102provides selective high resolution scanning during pauses in operationof the first LIDAR device 302 between complete scans provided by thefirst LIDAR device 302. In some embodiments, the two LIDAR devices 302,1102 can optionally be operated with distinguishable laser light sourcesthat can be separately detected via the optical sensors. For example,the two LIDAR devices 302, 1102 can include laser light sources atdistinct wavelengths that can be distinguished in the optical sensors soas to avoid confusion between the two even while operating the LIDARdevices 302, 1102 simultaneously. In some embodiments, the two LIDARsystem can avoid sensor confusion by operating each of the LIDAR devices302, 1102 simultaneously in distinct regions of the scanning zone thatare associated with distinct optical sensors. Thus, configurations canbe selected such that a single optical sensor does not simultaneouslyreceive reflected optical signals from pulses emitted from both LIDARdevices 302, 1102.

As used herein a “scanning zone” generally refers to a region of ascanned environment scanned by a single LIDAR device, or a combinationof LIDAR devices, that is completely sampled in a complete scan by theLIDAR device. That is, for a LIDAR device operated to continuouslyactively map its surroundings for reflective features, the scanning zoneis the complete region mapped before returning to map the same pointagain. Generally, the scanning zone referred to herein is defined withreference to the point of view of the LIDAR device in terms of azimuth(e.g., angle along the horizon) and altitude (e.g., angle perpendicularto the horizon) with respect to the point of view of the LIDAR device.Thus, the geographic location mapped by the scanning zone of a LIDARdevice is not fixed, but rather moves with the LIDAR device. Forexample, the scanning zone can be considered a bubble surrounding aparticular LIDAR device with dimensions defined by the maximum distancesensitivity of the LIDAR device.

In some embodiments, the azimuth range of the scanning zone can beapproximately 360 degrees, or a divisor thereof, such as 180, 90, 60,etc. to allow for 360 degree coverage by an even number of similar suchLIDAR devices. In some embodiments, the altitude range can extend from 0to 180 degrees. In some embodiments, the altitude range of the scanningzone can extend by an approximately equal number of degrees to eitherside of the horizon, such as 30 degrees either side of the horizon (60to 120 degrees altitude), 20 degrees either side of the horizon (70 to110 degrees altitude), etc. Of course, the altitude range can also be anincomplete coverage of the available 0 to 180 degree range that isbiased above or below the horizon, such as providing coverage up to 45degrees above the horizon and 15 degrees below the horizon (45 to 105degrees altitude), to take just one example. Of course, as with theazimuth ranges, the altitude ranges can be divided substantially equallybetween multiple LIDAR devices, such as in 30 degree sections, 20 degreesections, 15 degree sections, etc.

Furthermore, a complete scan is referred to herein as being completedwithin a scanning interval, which can be the time required forperforming a complete scan of the scanning zone. In other words, a givenpoint in the scanning zone is generally scanned on an interval given bythe scanning interval, with every other point in the scanning zone beingscanned in the interim.

FIG. 12 depicts a computer-readable medium configured according to anexample embodiment. In example embodiments, the example system caninclude one or more processors, one or more forms of memory, one or moreinput devices/interfaces, one or more output devices/interfaces, andmachine-readable instructions that when executed by the one or moreprocessors cause the system to carry out the various functions, tasks,capabilities, etc., described above.

As noted above, in some embodiments, the disclosed techniques can beimplemented by computer program instructions encoded on a non-transitorycomputer-readable storage media in a machine-readable format, or onother non-transitory media or articles of manufacture (e.g., theinstructions 115 stored on the data storage 114 of the computer system112 of vehicle 100). FIG. 12 is a schematic illustrating a conceptualpartial view of an example computer program product that includes acomputer program for executing a computer process on a computing device,arranged according to at least some embodiments presented herein.

In one embodiment, the example computer program product 1200 is providedusing a signal bearing medium 1202. The signal bearing medium 1202 mayinclude one or more programming instructions 1204 that, when executed byone or more processors may provide functionality or portions of thefunctionality described above with respect to FIGS. 1-11. In someexamples, the signal bearing medium 1202 can be a computer-readablemedium 1206, such as, but not limited to, a hard disk drive, a CompactDisc (CD), a Digital Video Disk (DVD), a digital tape, memory, etc. Insome implementations, the signal bearing medium 1202 can be a computerrecordable medium 1208, such as, but not limited to, memory, read/write(R/W) CDs, R/W DVDs, etc. In some implementations, the signal bearingmedium 1202 can be a communications medium 1210, such as, but notlimited to, a digital and/or an analog communication medium (e.g., afiber optic cable, a waveguide, a wired communications link, a wirelesscommunication link, etc.). Thus, for example, the signal bearing medium1202 can be conveyed by a wireless form of the communications medium1210.

The one or more programming instructions 1204 can be, for example,computer executable and/or logic implemented instructions. In someexamples, a computing device such as the computer system 112 of FIG. 1is configured to provide various operations, functions, or actions inresponse to the programming instructions 1204 conveyed to the computersystem 112 by one or more of the computer readable medium 1206, thecomputer recordable medium 208, and/or the communications medium 1210.

The non-transitory computer readable medium could also be distributedamong multiple data storage elements, which could be remotely locatedfrom each other. The computing device that executes some or all of thestored instructions could be a vehicle, such as the vehicle 200illustrated in FIG. 2. Alternatively, the computing device that executessome or all of the stored instructions could be another computingdevice, such as a server.

While various example aspects and example embodiments have beendisclosed herein, other aspects and embodiments will be apparent tothose skilled in the art. The various example aspects and exampleembodiments disclosed herein are for purposes of illustration and arenot intended to be limiting, with the true scope and spirit beingindicated by the following claims.

1. A method comprising: scanning by a light detection and ranging(LIDAR) device within a first region of a scanning zone while emittinglight pulses from the LIDAR device at a first pulse rate; scanning bythe LIDAR device within a second region of the scanning zone whileemitting light pulses from the LIDAR device at a second pulse rate thatis lower than the first pulse rate, wherein an angular resolution of thefirst region is based on the first pulse rate and an angular resolutionof the second region is based on the second pulse rate; adjusting thepulse rate of the LIDAR device based on whether the LIDAR device isscanning the first region or the second region; and receiving returninglight pulses corresponding to the light pulses emitted from the LIDARdevice.
 2. The method of claim 1, further comprising determiningdistance information based on time delays between the emitted lightpulses and corresponding returning light pulses and correspondingorientations of the LIDAR device.
 3. The method of claim 2, furthercomprising: analyzing the distance information; comparing the distanceinformation to a baseline map corresponding to the scanning zone; andbased on the comparison, determining the first region of the scanningzone.
 4. The method of claim 2, further comprising: analyzing thedistance information; based on the analysis of the distance information,identifying at least one feature of interest; and determining the firstregion of the scanning zone so as to include the at least one feature ofinterest.
 5. The method of claim 2, further comprising: analyzing thedistance information; based on the analysis of the distance information,ranking regions of the scanning zone based on at least one of a spatialfrequency or a temporal frequency; and based on the ranking, determiningthe first region of the scanning zone.
 6. The method of claim 2, furthercomprising: analyzing the distance information; based on the analysis ofthe distance information, identifying regions of the scanning zone withspatial resolution below an object detection resolution threshold; andbased on the identified regions, determining the first region of thescanning zone.
 7. The method of claim 1, further comprising: analyzingthe received returning light pulses to identify one or more obstacles inthe scanning zone interfering with a current path of an autonomousvehicle associated with the LIDAR device; determining a modified path ofthe autonomous vehicle that avoids the identified one or more obstacles;and instructing the autonomous vehicle to navigate along the modifiedpath.
 8. The method of claim 1, further comprising: determining apredicted position of one or more obstacles in motion, based on thereceived returning light pulses from the first region of the scanningzone, wherein the second region of the scanning zone is based on thepredicted position of the one or more obstacles in motion.
 9. The methodof claim 1, wherein the method is applied to a plurality of LIDARdevices, each LIDAR device comprising: a plurality of light sources; aplurality of light detectors; and a scanning mechanism configured toadjust an orientation of the LIDAR device while scanning within thescanning zone.
 10. A system comprising: a plurality of LIDAR devices,each LIDAR device comprising: a plurality of light sources operable toemit light pulses within a scanning zone; a plurality of lightdetectors; and a scanning mechanism configured to adjust an orientationof the LIDAR device while scanning within the scanning zone.
 11. Thesystem of claim 10, further comprising a controller, wherein thecontroller executes instructions stored in a memory so as to carry outoperations, the operations comprising: causing a scanning mechanism toscan at least one LIDAR device within a first region of a scanning zonewhile emitting light pulses from the LIDAR device at a first pulse rate;causing the scanning mechanism to scan the at least one LIDAR devicewithin a second region of the scanning zone while emitting light pulsesfrom the LIDAR device at a second pulse rate that is lower than thefirst pulse rate, wherein an angular resolution of the first region isbased on the first pulse rate and an angular resolution of the secondregion is based on the second pulse rate; adjusting the pulse rate ofthe at least one LIDAR device based on whether the at least one LIDARdevice is scanning the first region or the second region; and receivingreturning light pulses corresponding to the light pulses emitted fromthe at least one LIDAR device.
 12. The system of claim 11, wherein theoperations further comprise determining distance information based onthe received returning light pulses.
 13. The system according to claim11, wherein the operations further comprise: analyzing the receivedreturning light pulses to identify one or more obstacles in the scanningzone interfering with a current path of an autonomous vehicle associatedwith the plurality of LIDAR devices; determining a modified path of theautonomous vehicle that avoids the identified one or more obstacles; andinstructing the autonomous vehicle to navigate along the modified path.14. The system of claim 11, wherein the operations further comprise:determining a predicted position of one or more obstacles in motion,based on the received returning light pulses from the first region ofthe scanning zone, wherein the second region of the scanning zone isbased on the predicted position of the one or more obstacles in motion.15. A sensor system comprising: a controller, having one or moreprocessors and one or more forms of memory, wherein the one or moreprocessors are operable to execute instructions stored in the memory soas to carry out operations, the operations comprising: causing thesensor system to obtain an initial scan of a scanning zone according toa first mode of operation; based on the initial scan, determining a highreflectance feature; and in response to determining the high reflectancefeature, causing the sensor system to obtain a further scan of the highreflectance feature according to a second mode of operation, wherein thefirst mode of operation and the second mode of operation differ in atleast one of: a scan region of the scanning zone, pulse rate, angularresolution, spatial resolution, or temporal resolution.
 16. The sensorsystem of claim 15, wherein the operations further comprise determiningdistance information based on the initial scan and the further scan. 17.The sensor system according to claim 15, wherein the operations furthercomprise: analyzing the initial scan and the further scan to identifyone or more obstacles interfering with a current path of an autonomousvehicle associated with the sensor system; determining a modified pathof the autonomous vehicle that avoids the identified one or moreobstacles; and instructing the autonomous vehicle to navigate along themodified path.
 18. The sensor system of claim 15, wherein the operationsfurther comprise predicting a position of the high reflectance featurein motion, based on the initial scan, wherein the second mode ofoperation is determined based on the predicted position of the highreflectance feature in motion.
 19. The sensor system of claim 15,wherein the operations further comprise: analyzing the initial scan;based on the analysis, ranking regions of the scanning zone based on atleast one of a spatial frequency or a temporal frequency; and based onthe ranking, determining the scan region of the further scan.
 20. Thesensor system of claim 15, wherein the operations further comprise:analyzing the initial scan; based on the analysis, identifying regionsof the scanning zone with spatial resolution below an object detectionresolution threshold; and based on the identified regions, determiningthe scan region of the further scan.